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stainedglassgardens · 5 years ago

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Favourite films watched in 2019

I arranged them into broad categories – other than that they’re in no particular order.

Indie

Skate Kitchen (Crystal Moselle, 2018) 6 Balloons (Marja-Lewis Ryan, 2018) The Party’s Just Beginning (Karen Gillan, 2018) Thirteen (Catherine Hardwicke, 2003) Baise-moi (Virginie Despentes and Coralie Trinh Thi, 2000) Vazante (Daniela Thomas, 2017) Erasing Eden (Beth Dewey, 2016) The Seen and the Unseen (Sekala Niskala, Kamila Andini, 2017) Knock Down Ginger (Cleo Samoles-Little, 2016) The Garden (Sommerhaüser, Sonja Maria Kröner, 2017) Marlina the Murderer in Four Acts (Marlina Si Pembunuh dalam Empat Babak, Mouly Surya, 2017) Fish Tank (Andrea Arnold, 2009) Soldiers. Story From Ferentari (Soldații. Poveste din Ferentari, Ivana Mladenović, 2017)

Comedy

Dick (Andrew Fleming, 1999) The Breaker Upperers (Madeleine Sami and Jackie Van Beek, 2018) It Stains the Sands Red (Colin Minihan, 2016) Satanic Panic (Chelsea Stardust, 2019)

Classics

Wanda (Barbara Loden, 1970) House of Wax (Andre DeToth, 1953) Eve's Bayou (Kasi Lemmons, 1997) Germany Pale Mother (Deutschland bleiche Mutter, Helma Sanders-Brahms, 1980)

Horror

April and the Devil (Jake Hammond, 2018) Blackwood (Andrew Montague, 2019) The Crescent (Seth A Smith, 2017) Us (Jordan Peele, 2019) American Mary (Jen and Sylvia Soska, 2012) Midsommar (Ari Aster, 2019) Black Christmas (Bob Clark, 1974) The Devil's Passenger (Dave Bundtzen, 2018)

Science fiction

Born in Flames (Lizzie Borden, 1983) Evolution (Lucile Hadžihalilović, 2015) In Full Bloom (Maegan Houang, 2019)

Action

Destroyer (Karyn Kusama, 2018) Under the Silver Lake (David Robert Mitchell, 2018) Snatch (Guy Ritchie, 2000) Holiday (Isabella Eklöf, 2018)

Documentary

Our Daily Bread (Unser täglich Brot, Nikolaus Geyrhalter, 2005) Abducted in Plain Sight (Skye Borgman, 2017) Jane Fonda in Five Acts (Susan Lacy, 2018) Stories We Tell (Sarah Polley, 2012) The Decline of Western Civilization series (Penelope Spheeris, 1981, 1988 and 1998)

Full list of 273 films watched in 2018 under the cut!

January

Like Father (Lauren Miller Rogen, 2018)

Upgrade (Leigh Whannell, 2018)

Skate Kitchen (Crystal Moselle, 2018)

Never Been Kissed (Raja Gosnell, 1999)

Anomalisa (Charlie Kaufman and Duke Johnson, 2015)

Dick (Andrew Fleming, 1999)

The Black Balloon (Elissa Down, 2008)

Under the Silver Lake (David Robert Mitchell, 2018)

6 Balloons (Marja-Lewis Ryan, 2018)

Rosy (Jess Bond, 2018)

The Party’s Just Beginning (Karen Gillan, 2018)

The Rider (Chloé Zhao, 2017)

Snowpiercer (Bong Joon-ho, 2013)

Touch of Evil (Orson Welles, 1958)

Thirteen (Catherine Hardwicke, 2003)

Sadie (Megan Griffiths, 2018)

The Miseducation of Cameron Post (Desiree Akhavan, 2018)

Frida (Julie Taymor, 2002)

Fyre: The Greatest Pary That Never Happened (Chris Smith, 2019)

Time Share (Tiempo Compartido, Sebastián Hofmann, 2018)

The Stranger (Orson Welles, 1946)

Abducted in Plain Sight (Skye Borgman, 2017)

King of Thieves (James Marsh, 2018)

Malevolent (Olaf de Fleur, 2018)

Serena (Susanne Bier, 2014)

Baise-moi (Virginie Despentes and Coralie Trinh Thi, 2000)

And Breathe Normally (Andið Eðlilega, Ísold Uggadóttir, 2018)

Catwalk: Tales from the Cat Show Circuit (Aaron Hancox and Michael McNamara, 2018)

Santoalla (Andrew Becker and Daniel Mehrer, 2016)

Jane Fonda in Five Acts (Susan Lacy, 2018)

Mademoiselle Paradis (Licht, Barbara Albert, 2017)

The B-Side: Elsa Dorfman’s Portrait Photography (Errol Morris, 2016)

February

Matangi/Maya/M.I.A (Steve Loveridge, 2018)

Pride & Prejudice (Joe Wright, 2005)T

The Brain Hack (Joseph White, 2014)

Vazante (Daniela Thomas, 2017)

Tanglewood (Jordan Prosser, 2016)

Outfall (Suzi Ewing, 2018)

Pigskin (Jake Hammond, 2015)

The Funspot (Jake Hammond, 2015)

April and the Devil (Jake Hammond, 2018)

Smithereens (Susan Seidelman, 1982)

Can You Ever Forgive Me? (Marielle Heller, 2018)

Bus Stop (Joshua Logan, 1956)

Pink Plastic Flamingos (Colin West, 2017)

The Breaker Upperers (Madeleine Sami and Jackie Van Beek, 2018)

Amanda Knox (Rod Blackhurst and Brian McGinn, 2016)

Holy Hell (Will Allen, 2016)

Shoplifters (Manbiki Kazoku, Hirokazu Kore-eda, 2018)

Skin (Jordana Spiro, 2015)

A Night at the Garden (Marshall Curry, 2017)

Give Up the Ghost (Nathan Sam Long, 2018)

Last One Screaming (Matt Devino, 2017)

The Katy Universe (Patrick Muhlberger, 2018)

Roma (Alfonso Cuarón, 2018)

Did You Hear About the Morgans? (Marc Lawrence, 2009)

End Game (Rob Epstein and Jeffrey Friedman, 2018)

Behind the Curve (Daniel J. Clark, 2018)

Our Daily Bread (Unser täglich Brot, Nikolaus Geyrhalter, 2005)

92MARS (Ricardo Bernardini, 2018)

Construct (Kevin Margo, 2018)

Invaders (Daniel Prince, 2018)

March

Three Identical Strangers (Tim Wardle, 2018)

Dirty John: The Dirty Truth (Sara Mast, 2019)

Blackwood (Andrew Montague, 2019)

One (Luke Bradford, 2019)

God's Kingdom (Guy Soulsby, 2018)

Holiday (Isabella Eklöf, 2018)

Frigid (Joe Kicak, 2016)

Girl of the Sky (Ariel Martin, 2017)

Monitor (Matt Black and Ryan Polly, 2018)

Donoma (Evan Spencer Brace, 2018)

Perfect Blue (パーフェクトブル, Pāfekuto Burū, Satoshi Kon, 1997)

The Sermon (Dean Puckett, 2018)

Layer Cake (Matthew Vaughn, 2004)

Easy A (Will Gluck, 2010)

Generation Wealth (Lauren Greenfield, 2018)

The Rachel Divide (Laura Brownson, 2018)

The Place Beyond the Pines (Derek Cianfrance, 2012)

Burden (Timothy Marrinan and Richard Dewey, 2016)

What Will People Say (Hva vil folk si, Iram Haq, 2017)

Close Encounters of the Third Kind (Steven Spielberg, 1977)

Dear Zachary: A Letter to a Son About His Father (Kurt Kuenne, 2008)

Animal (Fabrice Le Nézet and Jules Janaud, 2017)

Capturing the Friedmans (Andrew Karecki, 2003)

The Fog of War: Eleven Lessons from the Life of Robert S. McNamara (Errol Morris, 2003)

April

Erasing Eden (Beth Dewey, 2016)

Destroyer (Karyn Kusama, 2018)

Unicorn Store (Brie Larson, 2019)

May the Devil Take You (Sebelum iblis menjemput, Timo Tjahjanto, 2018)

People in Cars (Daniel Lundh, 2017)

Presentation (Danielle Kampf, 2017)

Ink (Jamin Winans, 2009)

Hedgehog (Lindsey Copeland, 2016)

Koyaanisqatsi (Godfrey Reggio, 1982)

Wanda (Barbara Loden, 1970)

The Silence (John R. Leonetti, 2019)

24 Davids (Céline Baril, 2017)

The Frame (Jamin Winans, 2014)

The Talented Mr. Ripley (Anthony Minghella, 1999)

Baraka (Ron Fricke, 1992)

Wayne’s World (Penelope Spheeris, 1992)

Stories We Tell (Sarah Polley, 2012)

Born in Flames (Lizzie Borden, 1983)

Jesse’s Girl (M. Keegan Uhl, 2018)

I Walked With a Zombie (Jacques Tourneur, 1943)

Mary Goes Round (Molly McGlynn, 2017)

The Green Fog (Guy Maddin, Evan Johnson, and Galen Johnson, 2017)

Psycho (Alfred Hitchcock, 1960)

Someone Great (Jennifer Kaytin Robinson, 2019)

May

Ekaj (Cati Gonzalez, 2015)

Capernaum (Nadine Labaki, 2018)

Porcupine Lake (Ingrid Veninger, 2017)

The Decline of Western Civilization (Penelope Spheeris, 1981)

The Decline of Western Civilization Part II: The Metal Years (Penelope Spheeris, 1988)

The Decline of Western Civilization III (Penelope Spheeris, 1998)

Revolver (Guy Ritchie, 2005)

Pokémon: Detective Pikachu (Rob Letterman, 2019)

RocknRolla (Guy Ritchie, 2008)

Snatch (Guy Ritchie, 2000)

Lock, Stock and Two Smoking Barrels (Guy Ritchie, 1998)

The Seen and the Unseen (Sekala Niskala, Kamila Andini, 2017)

Nkosi Coiffure (Frederike Migom, 2015)

Speak Your Truth (Kris Erickson, 2018)

Incendies (Denis Villeneuve, 2010)

A.I. Rising (Lazar Bodrosa, 2018)

The Crescent (Seth A Smith, 2017)

Ring (リング, Ringu, Hideo Nakata, 1998)

Absences (Carole Laganière, 2013)

The Uninvited (Lewis Allen, 1944)

In Color (José Andrés Cardona, 2019)

Winners (Dan Bulla, 2018)

Jess (Daniel Hurwitz, 2018)

My First Time (Asaf Livni, 2018)

Murmur (Aurora Fearnley, 2018)

Pulsar (Aurora Fearnley, 2017)

Struck (Aurora Fearnley, 2017)

Samira (Lainey Richardson, 2018)

Despite Everything (A pesar de todo, Gabriela Tagliavini, 2019)

It Stains the Sands Red (Colin Minihan, 2016)

Satain Said Dance (Szatan kazał tańczyć, Katarzyna Rosłaniec, 2016)

Knock Down Ginger (Cleo Samoles-Little, 2016)

Gold (Cleo Samoles-Little, 2015)

Jane's Life (Cleo Samoles-Little, 2012)

4/4 (Kyle Sawyer, 2016)

Sugar Land (Lorenzo Lanzillotti, 2018)

The Idea of North (Albert Choi, 2018)

A Quiet Place (John Krasinski, 2018)

Dark Water (仄暗い水の底から, Honogurai Mizu no soko kara, Hideo Nakata, 2002)

Sound of My Voice (Zal Batmanglij, 2011)

Us (Jordan Peele, 2019)

The Perfection (Richard Shepard, 2018)

House of Wax (Andre DeToth, 1953)

June

We Have Always Lived in the Castle (Stacie Passon, 2018)

Always Be My Maybe (Nahnatchka Khan, 2019)

Gente que viene y bah (Patricia Font, 2019)

Period. End of Sentence. (Rayka Zehtabchi, 2018)

American Mary (Jen and Sylvia Soska, 2012)

The Boss (Ben Falcone, 2016)

Extremis (Dan Krauss, 2016)

E il cibo va (Food on the Go, Mercedes Cordova, 2017)

Last Night (Massy Tadjedin, 2010)

Murder Mystery (Kyle Newacheck, 2019)

Bead Game (Ishu Patel, 1977)

The Ceiling (Katto, Teppo Airaksinen, 2017)

Elisa & Marcela (Elisa y Marcela, Isabel Coixet, 2019)

Marlina the Murderer in Four Acts (Marlina Si Pembunuh dalam Empat Babak, Mouly Surya, 2017)

The Garden (Sommerhaüser, Sonja Maria Kröner, 2017)

Fast Color (Julia Hart, 2018)

The Tale of Iya (Iya Monogatari: Oku no Hito, Tetsuichiro Tsuta, 2013)

Chico and Rita (Chico y Rita, Tono Errando, Fernando Trueba and Javier

Mariscal, 2010)

Rafiki (Wanuri Kahiu, 2018)

Floating! (Das Floß!, Julia C. Kaiser, 2015)

The Quiet American (Phillip Noyce, 2002)

July

Keepers of the Magic (Vic Sarin, 2016)

Evolution (Lucile Hadžihalilović, 2015)

Mr. Holmes (Bill Condon, 2015)

The Long Dumb Road (Hannah Fidell, 2018)

Toni Erdmann (Maren Ade, 2016)

Life Overtakes Me (John Haptas and Kristine Samuelson, 2019)

The Milk System (Andreas Pilcher, 2017)

A Streetcar Named Desire (Elia Kazan, 1951)

The Texture of Falling (Maria Allred, 2019)

Family (Laura Steinel, 2018)

Sudden Fear (David Miller, 1952)

Identity Thief (Seth Gordon, 2013)

August

Point Break (Kathryn Bigelow, 1991)

In Full Bloom (Maegan Houang, 2019)

Blue Steel (Kathryn Bigelow, 1990)

The Eagles are a Country Music Band (Cody Wagner, 2018)

The Fifth Element (Luc Besson, 1997)

Hobbs & Shaw (David Leitch, 2019)

Coco (Lee Unkrich, 2017)

Bubba Ho-Tep (Don Coscarelli, 2002)

John Wick (Chad Stahelski, 2014)

Eve's Bayou (Kasi Lemmons, 1997)

I Don’t Protest, I Just Dance In My Shadow (Jessica Ashman, 2017)

My Cousin Rachel (Henry Koster, 1952)

Lifeline (Harry Jackson, 2018)

FOMI (Fear of Missing In) (Norbert Fodor, 2019)

Body at Brighton Rock (Roxanne Benjamin, 2019)

Koreatown (Grant Hyun, 2018)

A Report of Connected Events (Mischa Rozema, 2018)

Sundays (Mischa Rozema, 2015)

A King's Betrayal (David Bornstein, 2014)

Perception (Ilana Rein, 2018)

Germany Pale Mother (Deutschland bleiche Mutter, Helma Sanders-Brahms, 1980)

Men in Black International (F. Gary Gray, 2019)

Captive State (Rupert Wyatt, 2019)

Little Forest (리틀 포레스트, Liteul Poleseuteu, Yim Soon-rye, 2018)

September

What Keeps You Alive (Colin Minihan, 2018)

Grave Encounters (The Vicious Brothers, 2011)

Terrified (Aterrados, Demián Rugna, 2017)

Blade Runner (Ridley Scott, 1982)

Helen (Sandra Nettelbeck, 2009)

Colossal (Nacho Vigalondo, 2016)

Out of Blue (Carol Morley, 2018)

Taxi (تاکسی‎, Jafar Panahi, 2015)

Dear Ex (誰先愛上他的, Mag Hsu and Hsu Chih-yen, 2018)

Marguerite (Marianne Farley, 2019)

Birders (Otilia Portillo Padua, 2019)

Midsommar (Ari Aster, 2019)

Mansfield Park (Patricia Rozema, 1999)

Long Term Delivery (Jake Honig, 2018)

Game (Joy Webster, 2017)

Fish Tank (Andrea Arnold, 2009)

Foxfire (Annette Haywood-Carter, 1996)

October

Zombieland (Ruben Fleischer, 2009)

Under the Shadow ( زیر سایه, Babak Anvari, 2015)

Ghostbusters (Ivan Reitman, 1984)

Scream (Wes Craven, 1996)

Ghostbusters (Paul Feig, 2016)

Jaws (Steven Spielberg, 1975)

Rabid (David Cronenberg, 1977)

Rabid (The Soska Sisters, 2019)

In the Shadow of the Moon (Jim Mickle, 2019)

Benny Loves Killing (Ben Woodiwiss, 2018)

The Golem (Yoav & Doron Paz, 2018)

Eli (Ciarán Foy, 2019)

The Adversary (L’Adversaire, Nicole Garcia, 2002)

Satanic Panic (Chelsea Stardust, 2019)

The Devil and Father Amorth (William Friedkin, 2017)

Wounds (Babak Anvari, 2019)

Silent Hill (Christophe Gans, 2006)

Sleeping Beauty (Julia Leigh, 2011)

Black Christmas (Bob Clark, 1974)

The Shift (Francesco Calabrese, 2014)

The Baby (Kamran Chahkar, Lei Jim, 2012)

Intrusion (Jack Michel, 2013)

The Devil's Passenger (Dave Bundtzen, 2018)

Halloween (John Carpenter, 1978)

November

A Hijacking (Kapringen, Tobias Lindholm, 2012)

The Kitchen (Andrea Berloff, 2019)

The Hole in the Ground (Lee Cronin, 2019)

Assassination Nation (Sam Levinson, 2018)

Amy (Asif Kapadia, 2015)

Tell Me Who I Am (Ed Perkins, 2019)

Possessed (Curtis Bernhardt, 1947)

Terminally Happy (Adina Istrate, 2015)

The Glass Key (Stuart Heisler, 1942)

LuTo (Katina Medina Mora, 2015)

The Night of the Hunter (Charles Laughton, 1955)

Bikram: Yogi, Guru, Predator (Eva Orner, 2019)

December

Soldiers. Story From Ferentari (Soldații. Poveste din Ferentari, Ivana Mladenović, 2017)

John and Michael (John et Michael, Shira Avni, 2004)

High Tension (Haute Tension, Alexandre Aja, 2003)

Little Joe (Jessica Hausner, 2019)

The Matrix (The Wachowskis, 1999)

Finders Keepers (Bryan Carberry and Clay Tweel, 2015)

To Catch a Thief (Alfred Hitchcock, 1955)

My Buddha is Punk (Andreas Hartmann, 2016)

Little Miss Sumo (Matt Kay, 2018)

#films #watched in 2019 #2019 in review

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jbecksart · 6 years ago

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visiting artist assignment | nothingness / noise

assignment for Polly Becker, we were asked to illustrate “nothingness”

i thought of static, a void, eyes

#eye horror #eyes #white noise #static #wow so edgy #lmao what is graphic design for 200 #Polly Becker #inked lineart #mixed media #traditional and digital #this one i don't entirely hate #so here's an actual post

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sbuzelli · 5 years ago

Photo

Art by Polly Becker for #PLANADVISER Jul/Aug 2019 “The Word About Newsletters” Practice Development story. *** Image: Getting the message out. *** #editorialillustration #illustration #PollyBecker #artdirection #SooJinBuzelli #PAJA19 https://www.instagram.com/p/B1EIveUB341/?igshid=1owloool5x0vq

#planadviser #editorialillustration #illustration #pollybecker #artdirection #soojinbuzelli #paja19

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nwbeerguide · 2 years ago

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A blend of six unique barrel-aged beers, Firestone Walker Brewing releases XXVI.

Press Release

Robles, CA: Firestone Walker today unveiled “XXVI”—the latest edition of its storied Anniversary Ale, blended once again with the help of local winemakers.

“We’re talking about a ‘who’s who’ of Paso Robles winemakers,” said Brewmaster Matt Brynildson. “We’re fortunate to call them our neighbors and friends, and they never disappoint when it comes to our annual blending session.”

The result this year is XXVI—a masterwork of flavor and complexity, blended from six distinct ales aged in bourbon and brandy barrels, marking the brewery’s 26th anniversary.

“This latest Anniversary Ale is just super delicious and refined,” Brynildson said. “It’s driven by two barrel-aged stouts, but it’s much more than that. It has this perfect balancing point with a lot of spirits barrel character, and there’s an underlying freshness that elevates the mouthfeel.”

XXVI is rolling out now in limited quantities to all Firestone Walker markets, and is available at each brewery location and online at FirestoneBeer.com for a limited time.

An Annual Rite Firestone Walker’s collaboration with local winemakers dates back to 2006, when Brynildson was tasked with creating the inaugural Anniversary Ale to mark the brewery’s 10th anniversary year.

“It’s a tall order to blend all of these distinct beers into a harmonious whole,” Brynildson said. “That’s why we bring in the winemakers—they are practicing experts in the art of blending.”

This year’s blending session marked the 17th occasion of this annual summer rite. As always, the winemakers broke into teams to create their own candidate blends from a variety of barrel-aged components. “We offered up a perfect spectrum of beers to choose from: a light and a heavy stout, an imperial brown ale, a golden ale and two styles of barley wines,” Brynildson said.

Next, the winemakers and brewers blind tasted all of the candidate blends, then voted as a group to crown the winner that would become XXVI.

XXVI: The Final Blend “It was nice to have our classic Bravo brown ale back in the mix as a complement to our Parabola and Velvet Merkin stouts,” Brynildson said. “Heavy Things contributed a subtle dimension of brandy barrel character. Finally, Helldorado and Wheat Wine added a splash of freshness to the beer.”

Parabola - Aged in Bourbon Barrels (32%) Russian Imperial Stout

Velvet Merkin - Aged in Bourbon Barrels (32%) Milk Stout

Bravo - Aged in Bourbon Barrels (24%) Imperial Brown Ale

Heavy Things - Aged in Brandy Barrels (6%) Barley Wine Made in Collaboration with The Bruery

Helldorado - Aged in Bourbon Barrels (4%) Blonde Barley Wine

Wheat Wine - Aged in Bourbon Barrels (2%) Strong Golden Barley Wine Co-Fermented with White Wine Grape Juice

The Winemakers Valia From and Emily Miller - Desparada Wines Russell From - Herman Story Wines Along with Danny “Fullpint” Becker – Editor-in-Chief, TheFullpint.com This team created the winning blend that became “XXVI” Jordan Fiorentini and Kyle Gingras - Epoch Estate Wines Matt Trevisan and Ryan Merryweather - Linne Calodo Sherman Thacher - Thacher Winery Steve Martell - Kaleidos Bastien Leduc - Seven Oxen Scott Hawley - Torrin Wine Phil LaMontagne - TH Estate Wines Pete Turrone and Cameron Silzer - Booker Wines

From outside the wine world Brad Clark - Private Press Brewing Greg and Jarrett Koran – Brewmaster’s Collective Club Members Anthony and Patrick Patterson – Brewmaster’s Collective Club Members

Family joining the fun Polly Walker David Walker Nick Firestone

#California #Paso Robles #Firestone Walker Brewing Company

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ross24fps · 6 years ago

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Annette Nancarrow

Brian Chippendale

Caravaggio

Dash Shaw

David Firth

David Lynch

Dominick Rabrun

Egon Schiele

Erick M. Ramos

Francis Bacon

Friedensreich Hundertwasser

George Herriman

Henri Matisse

Henry Moore

J.M.W. Turner

Jean-Michel Basquiat

Jeremy Sorese

Jhonen Vasquez

John Lurie

Jonathan Lyndon Chase

Joost Swarte

Kathe Kollwitz

Keith Haring

Kevin Boehner

Kuzana Ogg

Liam Cobb

Marc Rothko

Marcel Dzama

Mike Kelley

Molly Fairhurst

Motomichi Nakamura

MrWolfu

Nikaido Ayumi

Parkston (a.k.a. title_it_yourself)

Pete Fowler

Philip Guston

Polly Becker

Reggie Pedro

Rembrandt

Rodney Alan Greenblat

Royal Robertson

Sam Falconer

Scatterzone

Shane Drinkwater

Stanley Donwood

Takashi Murakami

Tom Haugomat

Waneella

William Joyce

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daniloqp · 3 years ago

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Podcast: Playing the job market

https://theministerofcapitalism.com/blog/podcast-playing-the-job-market/

Increasingly, job seekers need to pass a series of ‘tests’ in the form of artificial intelligence games—just to be seen by a hiring manager. In this third, of a four-part miniseries on AI and hiring, we speak to someone who helped create these tests, we ask who might get left behind in the process and why there isn’t more policy in place. We also try out some of these tools ourselves.

We Meet:

Matthew Neale, Vice President of Assessment Products, Criteria Corp.

Frida Polli, CEO, Pymetrics

Henry Claypool, Consultant and former Obama Administration Member, Commission on Long-Term Care

Safe Hammad, CTO, Arctic Shores

Alexandra Reeve Givens, President and CEO, Center for Democracy and Technology

Nathaniel Glasser, Employment Lawyer, Epstein Becker Green

Keith Sonderling, Commissioner, Equal Employment Opportunity Commission (EEOC)

We Talked To:

Aaron Rieke, Managing Director, Upturn

Adam Forman, Employment Lawyer, Epstein Becker Green

Brian Kropp, Vice President Research, Gartner

Josh Bersin, Research Analyst

Jonathan Kestenbaum, Co-Founder and Managing Director, Talent Tech Labs

Frank Pasquale, Professor, Brooklyn Law School

Patricia (Patti) Sanchez, Employment Manager, MacDonald Training Center

Matthew Neale, Vice President of Assessment Products, Criteria Corp.

Frida Polli, CEO, pymetrics

Henry Claypool, Consultant and former Obama Administration Member, Commission on Long-Term Care

Safe Hammad, CTO, Arctic Shores

Alexandra Reeve Givens, President and CEO, Center for Democracy and Technology

Nathaniel Glasser, Employment Lawyer, Epstein Becker Green

Keith Sonderling, Commissioner, Equal Employment Opportunity Commission (EEOC)

Sounds From:

Science 4-Hire, podcast

Matthew Kirkwold’s cover of XTC’s, Complicated Game, https://www.youtube.com/watch?v=tumM_6YYeXs

Credits:

This miniseries on hiring was reported by Hilke Schellmann and produced by Jennifer Strong, Emma Cillekens, Anthony Green and Karen Hao. We’re edited by Michael Reilly.

Transcript

[TR ID]

Jennifer: Often in life …you have to “play the metaphorical game”… to get the win you might be chasing.

(sounds from Matthew Kirkwold’s cover of XTC’s Complicated Game, “And it’s always been the same.. It’s just a complicated game.. Gh – ah.. Game..”

Jennifer: But what if that game… was literal?

And what if winning at it could mean the difference between landing a job you’ve been dreaming of… or not.

Increasingly job seekers need to pass a series of “tests” in the form of artificial intelligence games… just to be seen by a hiring manager.

Anonymous Jobseeker: For me being a military veteran being able to take tests and quizzes or being under pressure is nothing for me, but I don’t know why the cognitive tests gave me anxiety, but I think it’s because I knew that it had nothing to do with software engineering that’s what really got me.

Jennifer: We met this job seeker in the first episode of this series …

She asked us to call her Sally because she’s criticizing the hiring methods of potential employers and she’s concerned about publishing her real name.

She has a graduate degree in information from Rutgers University in New Jersey, with specialties in data science and interaction design.

And Sally fails to see how solving a timed puzzle… or playing video games like tetris… have any real bearing on her potential to succeed in her field.

Anonymous Jobseeker: And I’m just like, what? I don’t understand. This is not relevant. So companies want to do diversity and inclusion, but you’re not doing diversity and inclusion when it comes to thinking, not everyone thinks the same. So how are you inputting that diversity and inclusion when you’re only selecting the people that can figure out a puzzle within 60 seconds.

Jennifer: She says she’s tried everything to succeed at games like the ones from Cognify she described… but without success.

She was rejected from multiple jobs she applied to that required these games.

Anonymous Jobseeker: I took their practice exams. I was practicing stuff on YouTube. I was using other peers and we were competing against each other. So I was like, all right, it’s not me because I studied for this. And I still did not quote unquote pass so…

Jennifer: I’m Jennifer Strong and in this third episode of our series on AI and hiring… we look at the role of games in the hiring process…

We meet some of the lead creators and distributors of these tools… and we share with them some feedback on their products from people like Sally.

Matthew Neale: The disconnect I think for this candidate was between what the assessment was getting the candidate to do and, and what was required or the perceptions about what was required on the job.

Jennifer: Matthew Neale helped create the Cognify tests she’s talking about.

Matthew Neale: I think the intention behind cognify is to look at people’s ability to learn, to process information, to solve problems. You know, I would say, I suppose that these kinds of skills are relevant in, in software design, particularly in, in software design where you’re going to be presented with complex difficult or unusual problems. And that’s the connection that I would draw between the assessment and the role.

Jennifer: So we tested some of these tools ourselves…

And we ask who might get left behind in the process… Plus, we find out why there isn’t more policy in place … and speak with one of the leading U-S regulators.

Keith Sonderling: There has been no guidelines. There’s been nothing specific to the use of artificial intelligence, whether it is resume screening, whether it’s targeting job ads or facial recognition or voice recognition, there has been no new guidelines from the EEOC since the technology has been created.

[SHOW ID]

Frida Polli: So I’m Frida Polli. I’m a former academic scientist. I spent 10 years at Harvard and MIT and I am the current CEO of a company called Pymetrics.

Jennifer: It’s an AI-games company that uses behavioral science and machine learning to help decide whether people are the right fit for a given job.

Frida Polli: I was a scientist who really loved the research I was doing. I was, at some point frustrated by the fact that it wasn’t there wasn’t a lot of applications, real world applications. So I went to business school looking for a problem, essentially that our science could help solve.

Jennifer: When I spoke to her earlier this year she called this her fundamental ‘aha’ moment in the path to creating her company.

Frida Polli: Essentially people were trying to glean cognitive, social and emotional aptitudes or what we call soft skills from a person’s resume, which didn’t seem like the optimal thing to do. If you’re trying to understand somebody more holistically, you can use newer behavioral science tools to do that. So ultimately just had this light bulb go off, thinking, okay, we know how to measure soft skills. We know how to measure the things that recruiters and candidates are looking to understand about themselves in a much more scientific objective way. We don’t have to tea-leaf-read off a resume.

Jennifer: The reason companies score job seekers is because they get way too many applications for open roles.

Frida Polli: You know if we could all wave our magic wand and not have to score people and magically distribute opportunity. I mean, my God, I’m all in all in. Right? And what we can do, I think, is make these systems as fair and predictive as possible, which was always kind of the goal.

Jennifer: She says Pymetrics does this using cognitive research… and they don’t measure hard skills…like whether someone can code… or use a spreadsheet.

Frida Polli: The fundamental premise is that we all sort of have certain pre predispositions and they’ll lead us to be more versus less successful. There’s been a lot of research showing that, you know, different cognitive, social and emotional or personality attributes do make people particularly well suited for, you know, role A and less well suited for role B. I mean, that research has, you know, predates Pymetrics and all we’ve done is essentially make the measurement of those things less reliant on self-report questionnaires and more reliant on actually measuring your behavior.

Jennifer: These games measure nine specific soft skills including attention and risk preference… which she says are important in certain jobs.

Frida Polli: It’s not super deterministic. It can change over time. But it’s a broad brush stroke of like, Hey, you know, if you tend to be like, let’s take me for a second, I tend to be somewhat impulsive, right. That would make me well disposed for certain roles, but potentially not others. So I guess what I would say is that both hard and soft skills are important for success in any particular role and the particular mix… it really depends on the role at hand, right?

Jennifer: Basically it works like this. — Employees who’ve been successful in a particular job a company is hiring for… are asked to play these games. That data gets compared against people already in a Pymetrics database… The idea is to build a model that identifies and ranks the skills unique to this group of employees… and to remove bias.

Jennifer: All of that gets compared against incoming job applicants… And it’s used by large global companies including KraftHeinz and AstraZeneca.

Another big player in this field is a company called Arctic Shores. Their games are used by the financial industry… and by large companies mainly in Europe.

Safe Hammad: The way we recruit was and in many cases is broken.

Jennifer: Safe Hammad is a co-founder and C-T-O of Arctic Shores.

Safe Hammad: But companies are recognizing that actually they can do better. They can do better on the predictability front to improve the bottom line for the companies. And also they can do better on the bias front as well. It’s a win-win situation by removing the bias, you get more suitable people, the right people in your company. I mean, what’s not to like?

Jennifer: The same as Pymetrics, Arctic Shores teases out personality traits via AI-based “video games.”

Safe Hammad: So, the way we measure something like sociability isn’t, ‘oh, you’re in a room and you want to go and talk to someone,’ or, you know, actually, you really wouldn’t realize, you know, there’s a few tasks where we ask you to choose left, choose right, and press you a little bit. And we come out with a measure of sociability. I mean, for me, it’s magic. I mean, I understand the science a little bit underneath. I certainly understand the mathematics, but it’s like magic. We actually don’t put you in a scenario. That’s anything to do with sociability. And yet, if you look at the stats the measurements are great.

Jennifer: He says the company’s tools are better than traditional testing methods, because the games can pull out traits of job applicants that might otherwise be hard to figure out. Like whether they’re innovative thinkers… something most candidates would probably just answer with yes if they were asked in a job interview.

Safe Hammad: When you ask questions they can be faked, you know, when I ask a question about, you know, how would you react if you’re in this position? You’re not thinking, oh, how would I react? You’re thinking, oh, what does the person asking me want me to say, that’s going to give me the best, best chance of getting that job. So without asking questions, by not asking questions, it’s a lot harder to fake and it’s a lot less subjective.

Jennifer: And to him, more data equals more objective hiring decisions.

Safe Hammad: It’s about seeing more in people before you bring in some of this information which can lead to bias so long as in the first stage of the process, you’re blind to their name, the school they went to what degree they got and you just look at the psychometrics, what is their potential for the role? That’s the first thing we need to answer.

Jennifer: He says games give everyone a fair chance to succeed regardless of their background. Both Pymetrics and Arctic Shores say their tools are built on well-established research and testing.

Safe Hammad: Now, these interactive tasks are very carefully crafted based on the scientific literature based on a lot of research and a lot of sweat that’s gone into making these, we actually can capture thousands of data points and, and a lot of those are very finely nuanced. And by using all that data, we’re able to really try and hone in on some of the behaviors that will match you to those roles.

Jennifer: And he says explainability of results is key in building trust in these new technologies.

Safe Hammad: So we do use AI, we do use machine learning to try and inform us to help us build that model. But the final model itself is more akin to what you would find a traditional psychometrics. It means that when it comes to our results, we can actually give you the results. We can tell you where you lie on the low to medium to high scale for creativity, for resilience, for learning agility. And we will stand by that.

Jennifer: And he says his company is also closely monitoring if the use of AI games — leads to better hiring decisions.

Safe Hammad: We’ll always be looking at the results, you know, has the outcome actually reflected what we said would happen. Are you getting better hires? Are they actually fulfilling your requirements? And, and better doesn’t necessarily mean, Hey, I’m more productive. Better can mean that they’re more likely to stay in the role for a year.

Jennifer: But not everyone is feeling so optimistic. Hilke Schellmann is a professor of journalism at NYU who covers the use of AI in hiring… She’s been playing a whole lot of these games… as well as talking to some critics. She’s here to help put it all into context.

Hilke: Yeah Jen.. AI-based video games are a recent phenomenon of the last decade or so. It’s a way for job applicants to get tested for a job in a more “fun” way…(well… that’s at least how vendors and employers pitch it), since they are playing “video games” instead of answering lots of questions in a personality test for example.

Jennifer: So, what types of games are you playing?

Hilke: So…. And I played a lot of these games. For one company, I had to essentially play a game of tetris and put different blocks in the right order. I also had to solve basic mathematical problems and test my language skills by finding grammar and spelling mistakes in an email.

Jennifer: But these aren’t like the ones you might play at home. There’s no sound in these games… and they look more like games from the 1980’s or early 90’s… and something that surprised me was all the biggest companies in this space seem to really be into games about putting air in balloons… What’s that about?

Hilke: Well.. The balloon game apparently measures your appetite for risk. When I played the game… I figured out pretty early on that yellow and red balloons pop after fewer pumps than the blue balloons, so I was able to push my luck with blue balloons and bank more money. But while I was playing I was also wondering if this game really measures my risk taking preferences in real life or if this only measures my appetite for risk in a video game.

Jennifer: Yeah, I could be a real daredevil when playing a game, but totally risk averse in real life.

Hilke: Exactly… And.. that’s one concern about AI-games. Another one is whether these games are actually relevant to a given job.

Jennifer: Ok, so help me understand then why companies are interested in using these games in the first place…

Hilke: So.. Jen From what I’ve learned these AI-games are most often used for entry level positions. So they are really popular with companies who hire recent college graduates. At that point, most job applicants don’t have a ton of work experience under their belts and personality traits start to play a larger role in finding the right people for the job. And oftentimes.. traits like agility or learning capabilities are becoming more important for employers.

Jennifer: Right… And companies are more likely to need to change up the way they do business now… so it means some skills wind up with a shorter shelf life.

Hilke: Yeah.. so in the past it may have been enough to hire a software developer with python skills, because that’s what a company needed for years to come. But these days, who knows how long a specific programming language is relevant in the workplace. Companies want to hire people who can re-train themselves and are not put off by change. And… these AI-games are supposed to help them find the right people.

Jennifer: So, that’s the sales pitch from the vendors. But Walmart.. (one of the biggest employers in the U-S)… they shared some of their findings with these technologies in a recent episode of an industry podcast called Science 4-Hire.

David Futrell: There’s no doubt that what we run is the biggest selection and assessment machine that’s ever existed on the planet. So, I mean, we test, everyday, between ten and fifteen thousand people and that’s just entry level hires for the stores.

Jennifer: That’s David Futrell. He is the senior director of organizational performance at Walmart.

David Futrell: When this machine learning idea first came out, I was very excited by it because, you know, it seemed to me like it would solve all of the problems that we had with prediction. And so we really got into it and did a lot of work with trying to build predictors using these machine based algorithms. And they work. But whenever it’s all said and done, they don’t really work any better than, you know, doing it the old fashioned way.

Jennifer: And he told the host of that podcast that Walmart acquired a company that was using a pure games based approach…

David Futrell: And uh we found that it just didn’t work well at all. I won’t mention the company, but it’s not the big company that it’s in this space. But they were purported to measure some underlying aspects of personality, like your willingness to take risks and so on.

Jennifer: And a concern with these and other AI hiring tools (that goes beyond whether they work better than what they’re replacing… is whether they work equally on different groups of people…

[sound of mouse clicking]

…including those with disabilities.

Henry Claypool: I’m just logging in now.

Jennifer: Henry Claypool is a disability policy analyst… and we asked him to play some of these games with us.

Henry Claypool: Okay, here we go…complete games.

Jennifer: He’s looking at one of the opening screens on a Pymetrics’ game. It asks players to select if they want to play a version that’s modified for color blindness, ADHD or dyslexia… or if they’d rather play a non-modified version.

Henry Claypool: Seems like that alone would be a legal challenge here.

Jennifer: He thinks it might even violate the Americans with Disabilities Act…. or A-D-A.

Henry Claypool: This is a pre-employment disclosure of disability, which could be used to discriminate against you. And so you’re putting the applicant on the horns of a dilemma, right — do I choose to disclose and seek an accommodation or do I just push through? The thing is you’re not allowed to ask an applicant about their disability before you make a job offer.

Jennifer: Claypool himself has a spinal cord injury from a skiing accident during his college years… It left him without the use of his legs and an arm.

But this hasn’t held back his career… He worked in the Obama administration and he helps companies with their disability policies.

Henry Claypool: The fear is that if I click one of these, I’ll disclose something that will disqualify me for the job, and if I don’t click on say dyslexia or whatever it is that I’ll be at a disadvantage to other people that read and process information more quickly. Therefore I’m going to fail either way or either way now my anxiety is heightened because I know I’m probably at a disadvantage.

Jennifer: In other words, he’s afraid if he discloses a disability this soon in the process… it might prevent him from getting an interview.

Henry Claypool: Ooops… am I… oh I’m pumping by the mouse.

Jennifer: Pymetrics’ suite of games starts with one where people get money to pump up balloons… and they have to bank it before a balloon pops.

Henry Claypool: Okay. Now carpal tunnel is setting in..

Jennifer: A few minutes into the game it’s getting harder for him.

Henry Claypool: I really hate that game. I just, I don’t see any logic in there at all. Knowing that I’m being tested by something that doesn’t want me to understand what it’s testing for makes me try to think through what it’s anticipating.

Jennifer: In other words, he has a dialogue going in his head trying to figure out what the system might want from him. And that distracts him from playing… such that he’s afraid he might not be doing so well.

And the idea that he and his peers have to play these games to get a job… doesn’t sit right with him.

He believes it’ll be harder for those with disabilities to get hired if that personal interaction early on in the process is taken away.

Henry Claypool: It’s really, it’s too bad that we’ve lost that human touch. And is there a way to use the merits of these analytic tools without leaving people feeling so vulnerable? And I feel almost scared and a little bit violated, right? That I’ve been probed in ways that I don’t really understand. And that feels pretty bad.

[music transition]

Alexandra Givens: When you think about the important role of access to employment, right? This is the gateway to opportunity for so many people. It’s a huge part, not only of economic stability, but also personal identity for people.

Jennifer: Alexandra Givens is the CEO of the Center for Democracy and Technology.

Alexandra Givens: And the risk that new tools are being deployed that are throwing up artificial barriers in a space that already has challenges with access is really troubling.

Jennifer: She studies the potential impacts of hiring algorithms on people with disabilities.

Alexandra Givens: When you’re doing algorithmic analysis, you’re looking for trends and you’re looking for kind of the statistical majority. And by definition, people with disabilities are outliers. So what do you do when an entire system is set up to not account for statistical outliers and not only not to account for them, but to actually end up intentionally excluding them because they don’t look like the statistical median that you’re gathering around.

Jennifer: She’s the daughter of the late Christopher Reeve, best known for his film roles as Superman… that is until he was paralyzed from the neck down from a horseback riding accident.

About 1 in 5 people in the U-S will experience disability at some point in their lives… and like Claypool she believes this type of hiring may exclude them.

Alexandra Givens: You hear people saying, well, this is actually the move toward future equality, right? HR run by humans is inherently flawed. People are going to judge you based on your hairstyle or your skin color, or whether you look like they’re friends or not their friends. And so let’s move to gamified tests, which actually don’t ask what someone’s resume is or doesn’t mean that they have to make good conversation in an interview. We want to see this optimistic story around the use of AI and employees are buying into that without realizing all of the ways in which these tools really can actually entrench discrimination and in a way even worse than human decision-making because they’re doing it at scale and they’re doing it in a way that’s harder to detect than individualized human bias because it’s hidden behind the decision-making of the machine.

Jennifer: We shared a specific hiring test with Givens… where you have to hit the spacebar as fast as you can. She says this game might screen out people with motor impairments — maybe even people who are older.

Alexandra Givens: They’re trying to use this as a proxy, but is that proxy actually a fair predictor of the skills required for the job? And I would say here, the answer is no for a certain percentage of the population. And indeed the way in which they’re choosing to test this is affirmatively going to screen out a bunch of people across the population in a way that’s deeply unfair.

Jennifer: And since job applicants don’t know what’s in these AI-games before they take a test… how do they know if they need to ask for an accommodation?

Also, she says people with disabilities might not want to ask for one anyway… if they’re afraid that could land their application into Pile B … and an employer may never look at Pile B.

Alexandra Givens: This isn’t just about discrimation against disabled people as a protected class. This is actually a question about the functioning of our society. And I think that’s pulling back that I think is one of the big systemic questions we need to raise here. Increasingly as we automate these systems and employers push to what’s most fastest, and most efficient, they’re losing the chance for people to actually show their qualifications and their ability to do the job and the context that they bring when they tell that story. And that is a huge loss. It’s a moral failing. I think it has legal ramifications, but that’s what we need to be scared about when we think about entrenching inequality in the workforce.

[Music transition]

Jennifer: After the break… a regulator in Washington answers why his agency hasn’t given any guidance on these tools.

But first… I’d like to invite you along for EmTech MIT in September. It’s Tech Review’s annual flagship conference and I’ll be there with the rest of the newsroom to help unpack the most relevant issues and technologies of our time. You can learn more at EmTech M-I-T dot-com.

We’ll be right back.

[MIDROLL]

Jennifer: I’m back with our reporting partner on this series, Hilke Schellmann… and as we just heard, access and fairness of these hiring tools for people with disabilities is a big concern…And…Hilke, did you expect to find this when you set out to do your research?

Hilke: Actually – that surprised me. I kinda expected to see a bias against women and people of color.. because we’ve seen that time and time again.. And it’s widely acknowledged that there’s a failing there… But I didn’t expect that people with disabilities would be at risk too. And all this made me ask another question. Are the algorithms in these games really making fair and unbiased decisions for all candidates?

Jennifer: And… so.. are the decisions fair?

Hilke: Well, actually no. Early on in my research.. I spoke to employment lawyers that deal with a lot of companies who are planning on doing business with AI-hiring vendors. They told me that they’re no strangers to problems with these algorithms…and they shared with me — something they haven’t shared publicly before.

Nathaniel Glasser: I think the underlying question was do these tools work? And I think the answer is, um, in some circumstances they do. And in some circumstances they don’t, a lot of it is, it is both vendor slash tool dependent and, also employer dependent and, and how they’re being put to work. And then practically, what’s the tool doing? And to the extent that we see problems and more specifically an adverse impact on a particular group, what are the solutions for addressing those issues?

Jennifer: Nathaniel Glasser is an employment lawyer in Washington, DC.

Nathaniel Glasser: So monitor, monitor, monitor, and if we see something wrong, let’s make sure that we have a plan of attack to address that. And that might be changing the algorithm in some sense, changing the inputs or if it doesn’t work, just making that decision to say, actually this tool is not right for us. It’s unfortunate that you know, we spent a little bit of money on it, but in the long run, it’s going to cause more problems than it’s worth. And so let’s cut ties now and move forward. And I’ve been involved in that situation before.

Jennifer: And he recalls a specific incident involving a startup vendor of AI-games.

Nathaniel Glasser: And unfortunately after multiple rounds in beta prior to going live, the tool demonstrated adverse impact against the female applicants and no matter the tweaks to the inputs and the traits and, and, and the algorithm itself, they couldn’t get confident that it wouldn’t continue to create this adverse impact. And they ultimately had to part ways and they went out to the market and they found something else that worked for them. Now that initial vendor, that was a startup five years ago, has continued to learn and grow and do quite well in the market. And, and I’m very confident, you know, that they learned from their mistakes and in working with other companies have figured it out.

Hilke: So, unfortunately the two lawyers signed a non-disclosure agreement and we don’t know which companies he’s talking about.

Jennifer: We only know that the company is still out there… and grew from a startup into an established player.

Hilke: And that could indicate that they fixed their algorithm or it could mean that no one’s looking…

Jennifer: And that’s something that comes up again and again. There’s no process that decides what AI-hiring tools are fair game… And anyone can bring any tool to market.

Hilke: Yeah… and The Equal Employment Opportunity Commission is the regulator of hiring and employment in the United States…. But they’ve been super quiet. So that’s probably why we’re now seeing individual states and cities starting to try to regulate the industry, but everyone is still kind of waiting for the commission to step in.

Jennifer: So we reached out to the E-E-O-C and connected with Keith Sonderling… He’s one of the commissioners who leads the agency.

Keith Sonderling: Well, since the 1960s, when the civil rights laws were enacted, our mission has been the same and that’s to make sure that everyone has an equal opportunity in the workplace… to enter the workplace and to succeed in the workplace.

Jennifer: Women, immigrants, people of color, and others have often had fewer workplace opportunities because of human bias… and despite its challenges, he believes AI has the potential to make some decisions more fair.

Keith Sonderling: So, I personally believe in the benefits of artificial intelligence in hiring. I believe that this is a technology that can fundamentally change how both employees and employers view their working relationship from everything of getting the right candidates to apply, to finding the actual right candidates to then when you’re working to making sure you’re in the job that is best suited for your skills or learn about other jobs that may, you may be even better at that you didn’t even know that a computer will help you understand. So there’s unlimited benefits here. Also, it can help diversify the workforce. So I think it is an excellent way to eliminate bias in recruiting and promotion, but also more importantly, it’s going to help employers find the right candidates who will have high level job satisfaction. And for the employees too, they will find the jobs that are right for them. So essentially it’s a wonderful matchmaking service.

Jennifer: But he’s also aware of the risks. He believes bad actors could exclude people like older workers… by doing things like programming a resume parser to reject resumes from people with a certain amount of experience.

And he says the tools themselves could also discriminate…. unintentionally.

Keith Sonderling: For instance, if an employer wants to use AI to screen through 500,000 resumes of workers to find people who live nearby. So they’re not late to work, say it’s a transportation company and the buses need to leave on time. So I’m only going to pick people who live in one zip code over from my terminal. And you know, that may exclude a whole protected class of people based on the demographics of that zip code. So the law will say that that person who uses AI for, intentionally, for wrong versus an employer who uses it for the right reasons, but winds up violating the law because they have that disparate impact based on those zip codes are equally held liable. So there’s a lot of potential liability for using AI unchecked.

Jennifer: Unintentional discrimination is called disparate impact… and it’s a key thing to watch in this new age of algorithmic decision making.

But…with these systems… how do you know for sure you’re being assessed differently? When most of the laws and guidelines that steer the agency were established more than 40 years ago… it was much easier for employees to know when and how they were being evaluated.

Keith Sonderling: Well, that could be potentially the first issue of using AI in the hiring process is that the employee may not even know they’re being subject to tests. They may not even know a program is monitoring their facial expressions as part of the interview. So that is a very difficult type of discrimination to find if you don’t even know you’re being discriminated against, how could you possibly bring a claim for discrimination?

Jennifer: Sonderling says that employers should also think long and hard about using AI tools that are built on the data of their current workforce.

Keith Sonderling: Is it going to have a disparate impact on different protected classes? And that is the number one thing employers using AI should be looking out for is the ideal employee I’m looking for? Is that just based on my existing workforce, which may be of a certain race, gender, national origin. And am I telling the computer that’s only who I’m looking for? And then when you get 50 resumes and they’re all identical to your workforce, there’s going to be some significant problems there because essentially the data you have fed that algorithm is only looking towards your existing workforce. And that is not going to create a diverse workforce with potentially workers from all different categories who can actually perform the jobs.

Jennifer: Experts we’ve talked to over the course of this reporting believe there’s enough evidence that some of these tools do not work as advertised and potentially harm women, people of color, people with disabilities and other protected groups… and they’ve criticized the agency’s lack of action and guidance.

The last hearing it held on big data? was in 2016… and a whole lot has changed with this technology since then.

And so we asked the commissioner about that.

Jennifer: And we wanted to understand how that fits with the agency’s mission…

Keith Sonderling: Well, my personal belief is that the EEOC is more than just an enforcement agency. Yes, we are a civil law enforcement agency. That’s required by law to bring investigations and to bring federal lawsuits. But part of our mission is to educate employees and employers. And this is an area where I think the EEOC should take the lead within the federal government.

Jennifer: What might be surprising here is this question of whether these tools work as advertised and pick the best people? That Isn’t the agency’s concern…

Keith Sonderling: Companies have been using similar assessment tests for a very long time and whether or not those tests are actually accurate and predict success of an employee, you know, that is beyond the scope of our job here at the EEOC. The only thing that the EEOC is concerned with when these tests are being instituted, are, is it discriminating against a protected class? That is our purpose. That is our responsibility and whether or not the tools actually work and whether not, it can computer can figure out is this employee in this position, in this location going to be an absolute superstar versus, you know, this employee in this location who should be doing these tasks, is that going to make them happy and going to make them productive for a company that’s beyond the scope of federal EEO law.

Jennifer: But.. If an AI tool passes a disproportionate number of men vs women, the agency can start investigating. And then, that question of whether the tool works or not, may become an important part of the investigation.

Keith Sonderling: It becomes very relevant when the results of the test have a disparate impact on a certain protected characteristic. So say if a test, a cognitive test, for some reason, excludes females, as an example, you know, then the employer would have to show if they want to move forward with that test and validate that test, they would then need to show that there is a business need and is job related that the tests we’re giving is excluding females. And, you know, that is a very difficult burden for employers to prove. And it can be very costly as well.

Jennifer: He’s contemplating something that’s called a Commissioner’s charge, which is a move that would allow him to force the agency to start an investigation… and he’s asking the public for help.

Keith Sonderling: So if an individual commissioner believes that discrimination is occurring, any areas of the laws we enforce, whether it’s disability, discrimination, sex discrimination, or here, AI discrimination, we can file a charge against the company ourselves and initiate an investigation. Now to do that, we need very credible evidence, and we need people to let us know this is happening, whether it’s a competitor in an industry, or whether it’s an individual employee who is afraid to come forward in their own name, but may be willing to allow a commissioner to go, or many commissioner charges have begun historically off watching the news, reading a newspaper. So there’s a lot of ways that the EEOC can get involved here. And that’s something I’m very interested in doing.

Jennifer: In the meantime, individual states and cities are starting to try to regulate the use of AI in hiring on their own… having a patchwork of laws that differ state by state can make it a whole lot harder for everyone to navigate an emerging field.

[music]

Jennifer: Next episode… what happens when AI interviews AI? We wrap up this series with a look at how people are gaming these systems… From advice on what a successful resume might look like…to classes on YouTube about how to up your odds of getting through A-I gatekeepers.

Narrator: My aim today is to help you get familiar and comfortable with this gamified assessment. In this game you’re presented with a number of balloons individually that you’re required to pump. Try the balloon game now.

[CREDITS]

Jennifer: This miniseries on hiring was reported by Hilke Schellmann and produced by me, Emma Cillekens, Anthony Green and Karen Hao. We’re edited by Michael Reilly.

Thanks for listening… I’m Jennifer Strong.

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stainedglassgardens · 5 years ago

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In a surprising turn of events, I have watched more films in the past six months than I ever have before, bringing the total to 171 from the first of January, 2019. Recap:

Like Father (Lauren Miller Rogen, 2018)

Upgrade (Leigh Whannell, 2018)

Skate Kitchen (Crystal Moselle, 2018)

Never Been Kissed (Raja Gosnell, 1999)

Anomalisa (Charlie Kaufman and Duke Johnson, 2015)

Dick (Andrew Fleming, 1999)

The Black Balloon (Elissa Down, 2008)

Under the Silver Lake (David Robert Mitchell, 2018)

6 Balloons (Marja-Lewis Ryan, 2018)

Rosy (Jess Bond, 2018)

The Party’s Just Beginning (Karen Gillan, 2018)

The Rider (Chloé Zhao, 2017)

Snowpiercer (Bong Joon-ho, 2013)

Touch of Evil (Orson Welles, 1958)

Thirteen (Catherine Hardwicke, 2003)

Sadie (Megan Griffiths, 2018)

The Miseducation of Cameron Post (Desiree Akhavan, 2018)

Frida (Julie Taymor, 2002)

Fyre: The Greatest Pary That Never Happened (Chris Smith, 2019)

Time Share (Tiempo Compartido, Sebastián Hofmann, 2018)

The Stranger (Orson Welles, 1946)

Abducted in Plain Sight (Skye Borgman, 2017)

King of Thieves (James Marsh, 2018)

Malevolent (Olaf de Fleur, 2018)

Serena (Susanne Bier, 2014)

Baise-moi (Virginie Despentes and Coralie Trinh Thi, 2000)

And Breathe Normally (Andið Eðlilega, Ísold Uggadóttir, 2018)

Catwalk: Tales from the Cat Show Circuit (Aaron Hancox and Michael McNamara, 2018)

Santoalla (Andrew Becker and Daniel Mehrer, 2016)

Jane Fonda in Five Acts (Susan Lacy, 2018)

Mademoiselle Paradis (Licht, Barbara Albert, 2017)

The B-Side: Elsa Dorfman’s Portrait Photography (Errol Morris, 2016)

Matangi/Maya/M.I.A (Steve Loveridge, 2018)

Pride & Prejudice (Joe Wright, 2005)

The Brain Hack (Joseph White, 2014)

Vazante (Daniela Thomas, 2017)

Tanglewood (Jordan Prosser, 2016)

Outfall (Suzi Ewing, 2018)

Pigskin (Jake Hammond, 2015)

The Funspot (Jake Hammond, 2015)

April and the Devil (Jake Hammond, 2018)

Smithereens (Susan Seidelman, 1982)

Can You Ever Forgive Me? (Marielle Heller, 2018)

Bus Stop (Joshua Logan, 1956)

Pink Plastic Flamingos (Colin West, 2017)

The Breaker Upperers (Madeleine Sami and Jackie Van Beek, 2018)

Amanda Knox (Rod Blackhurst and Brian McGinn, 2016)

Holy Hell (Will Allen, 2016)

Shoplifters (Manbiki Kazoku, Hirokazu Kore-eda, 2018)

Skin (Jordana Spiro, 2015)

A Night at the Garden (Marshall Curry, 2017)

Give Up the Ghost (Nathan Sam Long, 2018)

Last One Screaming (Matt Devino, 2017)

The Katy Universe (Patrick Muhlberger, 2018)

Roma (Alfonso Cuarón, 2018)

Did You Hear About the Morgans? (Marc Lawrence, 2009)

End Game (Rob Epstein and Jeffrey Friedman, 2018)

Behind the Curve (Daniel J. Clark, 2018)

Our Daily Bread (Unser täglich Brot, Nikolaus Geyrhalter, 2005)

92MARS (Ricardo Bernardini, 2018)

Construct (Kevin Margo, 2018)

Invaders (Daniel Prince, 2018)

Three Identical Strangers (Tim Wardle, 2018)

Dirty John: The Dirty Truth (Sara Mast, 2019)

Blackwood (Andrew Montague, 2019)

One (Luke Bradford, 2019)

God's Kingdom (Guy Soulsby, 2018)

Holiday (Isabella Eklöf, 2018)

Frigid (Joe Kicak, 2016)

Girl of the Sky (Ariel Martin, 2017)

Monitor (Matt Black and Ryan Polly, 2018)

Donoma (Evan Spencer Brace, 2018)

Perfect Blue (パーフェクトブル, Pāfekuto Burū, Satoshi Kon, 1997)

The Sermon (Dean Puckett, 2018)

Layer Cake (Matthew Vaughn, 2004)

Easy A (Will Gluck, 2010)

Generation Wealth (Lauren Greenfield, 2018)

The Rachel Divide (Laura Brownson, 2018)

The Place Beyond the Pines (Derek Cianfrance, 2012)

Burden (Timothy Marrinan and Richard Dewey, 2016)

What Will People Say (Hva vil folk si, Iram Haq, 2017)

Close Encounters of the Third Kind (Steven Spielberg, 1977)

Dear Zachary: A Letter to a Son About His Father (Kurt Kuenne, 2008)

Animal (Fabrice Le Nézet and Jules Janaud, 2017)

Capturing the Friedmans (Andrew Karecki, 2003)

The Fog of War: Eleven Lessons from the Life of Robert S. McNamara (Errol Morris, 2003)

Erasing Eden (Beth Dewey, 2016)

Destroyer (Karyn Kusama, 2018)

Unicorn Store (Brie Larson, 2019)

May the Devil Take You (Sebelum iblis menjemput, Timo Tjahjanto, 2018)

People in Cars (Daniel Lundh, 2017)

Presentation (Danielle Kampf, 2017)

Ink (Jamin Winans, 2009)

Hedgehog (Lindsey Copeland, 2016)

Koyaanisqatsi (Godfrey Reggio, 1982)

Wanda (Barbara Loden, 1970)

The Silence (John R. Leonetti, 2019)

24 Davids (Céline Baril, 2017)

The Frame (Jamin Winans, 2014)

The Talented Mr. Ripley (Anthony Minghella, 1999)

Baraka (Ron Fricke, 1992)

Wayne’s World (Penelope Spheeris, 1992)

Stories We Tell (Sarah Polley, 2012)

Born in Flames (Lizzie Borden, 1983)

Jesse’s Girl (M. Keegan Uhl, 2018)

I Walked With a Zombie (Jacques Tourneur, 1943)

Mary Goes Round (Molly McGlynn, 2017)

The Green Fog (Guy Maddin, Evan Johnson, and Galen Johnson, 2017)

Psycho (Alfred Hitchcock, 1960)

Someone Great (Jennifer Kaytin Robinson, 2019)

Ekaj (Cati Gonzalez, 2015)

Capernaum (Nadine Labaki, 2018)

Porcupine Lake (Ingrid Veninger, 2017)

The Decline of Western Civilization (Penelope Spheeris, 1981)

The Decline of Western Civilization Part II: The Metal Years (Penelope Spheeris, 1988)

The Decline of Western Civilization III (Penelope Spheeris, 1998)

Revolver (Guy Ritchie, 2005)

Pokémon: Detective Pikachu (Rob Letterman, 2019)

RocknRolla (Guy Ritchie, 2008)

Snatch (Guy Ritchie, 2000)

Lock, Stock and Two Smoking Barrels (Guy Ritchie, 1998)

The Seen and the Unseen (Sekala Niskala, Kamila Andini, 2017)

Nkosi Coiffure (Frederike Migom, 2015)

Speak Your Truth (Kris Erickson, 2018)

Incendies (Denis Villeneuve, 2010)

A.I. Rising (Lazar Bodrosa, 2018)

The Crescent (Seth A Smith, 2017)

Ring (リング, Ringu, Hideo Nakata, 1998)

Absences (Carole Laganière, 2013)

The Uninvited (Lewis Allen, 1944)

In Color (José Andrés Cardona, 2019)

Winners (Dan Bulla, 2018)

Jess (Daniel Hurwitz, 2018)

My First Time (Asaf Livni, 2018)

Murmur (Aurora Fearnley, 2018)

Pulsar (Aurora Fearnley, 2017)

Struck (Aurora Fearnley, 2017)

Samira (Lainey Richardson, 2018)

Despite Everything (A pesar de todo, Gabriela Tagliavini, 2019)

It Stains the Sands Red (Colin Minihan, 2016)

Satain Said Dance (Szatan kazał tańczyć, Katarzyna Rosłaniec, 2016)

Knock Down Ginger (Cleo Samoles-Little, 2016)

Gold (Cleo Samoles-Little, 2015)

Jane's Life (Cleo Samoles-Little, 2012)

4/4 (Kyle Sawyer, 2016)

Sugar Land (Lorenzo Lanzillotti, 2018)

The Idea of North (Albert Choi, 2018)

A Quiet Place (John Krasinski, 2018)

Dark Water (仄暗い水の底から, Honogurai Mizu no soko kara, Hideo Nakata, 2002)

Sound of My Voice (Zal Batmanglij, 2011)

Us (Jordan Peele, 2019)

The Perfection (Richard Shepard, 2018)

House of Wax (Andre DeToth, 1953)

We Have Always Lived in the Castle (Stacie Passon, 2018)

Always Be My Maybe (Nahnatchka Khan, 2019)

Gente que viene y bah (Patricia Font, 2019)

Period. End of Sentence. (Rayka Zehtabchi, 2018)

American Mary (Jen and Sylvia Soska, 2012)

The Boss (Ben Falcone, 2016)

Extremis (Dan Krauss, 2016)

E il cibo va (Food on the Go, Mercedes Cordova, 2017)

Last Night (Massy Tadjedin, 2010)

Murder Mystery (Kyle Newacheck, 2019)

Bead Game (Ishu Patel, 1977)

The Ceiling (Katto, Teppo Airaksinen, 2017)

Elisa & Marcela (Elisa y Marcela, Isabel Coixet, 2019)

Marlina the Murderer in Four Acts (Marlina Si Pembunuh dalam Empat Babak, Mouly Surya, 2017)

The Garden (Sommerhaüser, Sonja Maria Kröner, 2017)

Fast Color (Julia Hart, 2018)

The Tale of Iya (Iya Monogatari: Oku no Hito, Tetsuichiro Tsuta, 2013)

Chico and Rita (Chico y Rita, Tono Errando, Fernando Trueba and Javier Mariscal, 2010)

Rafiki (Wanuri Kahiu, 2018)

Floating! (Das Floß!, Julia C. Kaiser, 2015)

The Quiet American (Phillip Noyce, 2002)

#watched in 2019 #films #yeah i don't believe it either

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dergarabedian · 6 years ago

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Una perfecta desconocida conmovió una visita imprevista en Moscú. Me refiero a Carola Neher, una actriz de la cual ignoraba de su existencia hasta el 10 de octubre de 2018.

Ese día venía mal en cuanto a los paseos. Estaba de vacaciones en la capital de Rusia junto a mi hija Agustina. Como siempre hago cuando salgo de viaje con ella, repartimos la jornada entre puntos turísticos de interés para cada uno.

Algunas veces las visitas son alternativos (un lugar elegido por Agustina, el siguiente por mí) o consecutivos (dos de ellas, dos míos).

Siempre hacemos viajes sentados en automóviles o medios de transporte público, para descansar entre los diferentes puntos a visitar.

Pero ese miércoles nublado y frío en Moscú venía mal: una combinacón fallida entre trolebuses, un templo que demoró media hora en abrirse y donde no se podían tomar fotos, un viaje muy lento en automóvil por una avenida embotellada, un museo que justo ese miércoles estaba imprevistamente cerrado…

Cuando nos íbamos de ese museo al que no habíamos podido ingresar, Agustina revisó su lista de lugares a visitar y observó que relativamente cerca había un organización internacional de derechos humanos, llamada Memorial, donde se exhibía una muestra sobre una actriz alemana de la primera mitad del siglo XX que falleció en la ex Unión Soviética.

Caminamos algo más de un kilómetro por calles y avenidas moscovitas hasta llegar al Memorial Internacional, donde conocí la vida de Carola Neher, una emigrante alemana atrapada entre dos extremos ideológicos.

La exposición se llama “El teatro de la vida de Carola Neher”, y está apoyada por el Instituto Goethe de Moscú.

La muestra ilumina los aspectos más claros y más oscuros de la vida de esta destacada actriz de teatro de la República de Weimar.

A principios de la década de 1920, Carola, nacida en el año 1900, era una joven de una familia buena posición económica de la ciudad bávara de Munich, que huyó de su hogar y abandonó su trabajo estable en un banco para convertirse en una de las actrices más famosas de la República de Weimar.

Durante sus breves diez años de carrera, interpretó innumerables papeles, se convirtió en el intérprete elegido del famoso escritor Bertolt Brecht (en la foto inferior) y tomó los escenarios europeos por asalto.

Su imagen adornaba las portadas de revistas de moda y tarjetas de cigarrillos.

Carola conducía un automóvil, subía a la torre de radio de Berlín, y simbolizaba así una nueva condición de la mujer, más independiente y emancipada.

La primera sección de la exhibición, diseñada en blanco, presenta fotografías, recortes de periódicos y material biográfico que ilustran los años de la adolescencia de Carola y su rápido ascenso como celebrada estrella de teatro en la década de 1920.

La adaptación de G. W. Pabst de la Threepenny Opera de Brecht (1931) con su papel de Polly Peachum también se puede ver en la muestra.

Pero las paredes y los pisos de la muestra se vuelven negros: la vida de Carola cambió bruscamente cuando se enamoró del comunista rumano Anatol Becker y se mudó a Moscú con él.

En ese momento, muchos militantes de izquierda occidentales vieron a la Unión Soviética como el último baluarte del comunismo.

Pero sus expectativas románticas fueron decepcionadas: la comida se racionaba incluso en Moscú, el hambre arrasaba en todo el país. Sólo la nomenklatura podía permitirse una vida “normal”.

En 1934, Carola, junto con escritores como Lion Feuchtwanger y Heinrich Mann, firmó un manifiesto contra la anexión de la región del Sarre por parte de la Alemania de Hitler.

Como todos los demás signatarios, su ciudadanía alemana fue quitada.

Como ella no poseía un pasaporte soviético, Carola de repente era apátrida.

La exposición muestra no solo fotos de la actriz, sino también grabaciones de sonido del archivo privado del hijo de Carola, Georg Becker.

Carola buscó desesperadamente trabajo de actuación, manteniéndose a flote con conciertos y transmisiones de radio para la diáspora alemana.

En 1936 fue arrestada junto con Anatol Becker bajo sospecha de conspiración trotskista.

Poco después, el gobierno del dictador comunista Stalin anunció las grandes purgas.

En 1937, Becker fue asesinado y Carola condenada a diez años de prisión.

El 26 de junio de 1942 murió de tifus en la prisión de tránsito de Sol-Ilezk en los Urales del Sur.

Hasta el final de su vida, se mantuvo en forma con la gimnasia, recitaba poesía y escribía cartas en periódicos.

Más que nada, buscaba descubrir el paradero de su hijo Georg, nacido en 1934.

El hecho de que la historia de vida de esta víctima de la represión stalinista sea conocida hoy se debe en gran parte a la dedicación de su hijo.

Georg Becker, que creció en un orfanato soviético, solo se enteró de las identidades de sus padres después de la muerte de Stalin.

En 1959, recibió un certificado de rehabilitación para su madre. Gracias al apoyo de Lew Kopelew, Heinrich Böll y otras luminarias alemanas de la política y las artes, pudo viajar a Alemania occidental en la década del 70.

Durante más de treinta años, recopiló toda la información que pudo encontrar sobre sus padres y las personas que los rodeaban.

Esta abundancia de materiales permite a los visitantes de la exposición de Carola Neher realizar un viaje desde el apogeo de la República de Weimar hasta las celdas de los campos penales soviéticos.

Además de los recortes de los periódicos alemanes y soviéticos de las décadas de 1920 y 1930, la exposición también incluye cartas y poemas de Brecht.

La historia privada y pública entrelazada se convierte así directamente en tangible para los visitantes.

Sin embargo, al final de la exposición, solo los documentos oficiales pueden “informar” sobre los últimos días de Carola.

Los contrastes de esta vida son conmovedores: Carola encarnó a Cleopatra y Eliza Doolittle, Polly Peach y Katharina de The Taming of the Shrew, y a escritos de Shakespeare y Brecht, Shaw y Klabund, Hauptmann y Eden von Horvath.

Casi todas sus heroínas se distinguen por su vivacidad, resistencia y fuerza mental, rasgos inherentes a la propia actriz, que eran tan necesarios para ella en la última década de su vida, cuando tuvo que desempeñar un papel en una obra de teatro protagonizada por directores no teatrales.

Carola fue la encarnación no solo de la actriz del nuevo teatro experimental, sino también de la nueva mujer moderna: le gustaban los deportes, conducía un automóvil y pilotaba el avión.

Sin embargo, una de las actrices más talentosas de la historia del teatro alemán murió de tifus en una prisión soviética.

La exposición cuenta la tragedia de una persona creativa sobresaliente que huyó de una dictadura y murió en otra.

En la muestra hay materiales únicos: documentos, fotos, videos y grabaciones de audio de los archivos de Alemania y Rusia, la colección familiar de los descendientes de Carola y la colección de museos del Memorial Internacional.

La exhibición conmueve porque permite conocer la biografía de un individuo tan de cerca y en detalle.

Y el mismo impacto se da cuando se da cuenta de que el 70 por ciento de todos los inmigrantes de Alemania fueron víctimas de las “purgas” estalinistas.

Por lo tanto, esta exposición muestra mucho más que el trágico camino de la estrella del cielo teatral y la sociedad de Berlín.

Cuando nos retiramos del lugar, agradecí a Agustina por su elección, que enderezó ese día y lo convirtió en inolvidable gracias a Carola Neher.

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Carola Neher, actriz que huyó de una dictadura y murió en otra Una perfecta desconocida conmovió una visita imprevista en Moscú. Me refiero a Carola Neher, una actriz de la cual ignoraba de su existencia hasta el 10 de octubre de 2018.

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