#Ethics in AI

[succliberation]

The biggest dataset used for AI image generators had CSAM in it

Link the original tweet with more info

The LAION dataset has had ethical concerns raised over its contents before, but the public now has proof that there was CSAM used in it.

The dataset was essentially created by scraping the internet and using a mass tagger to label what was in the images. Many of the images were already known to contain identifying or personal information, and several people have been able to use EU privacy laws to get images removed from the dataset.

However, LAION itself has known about the CSAM issue since 2021.

LAION was a pretty bad data set to use anyway, and I hope researchers drop it for something more useful that was created more ethically. I hope that this will lead to a more ethical databases being created, and companies getting punished for using unethical databases. I hope the people responsible for this are punished, and the victims get healing and closure.

[nando161mando]

[prismetric-technologies]

Building Ethical AI: Challenges and Solutions

Artificial Intelligence (AI) is transforming industries worldwide, creating opportunities for innovation, efficiency, and growth. According to recent statistics, the global AI market is expected to grow from $59.67 billion in 2021 to $422.37 billion by 2028, at a CAGR of 39.4% during the forecast period. Despite the tremendous potential, developing AI technologies comes with significant ethical challenges. Ensuring that AI systems are designed and implemented ethically is crucial to maximizing their benefits while minimizing risks. This article explores the challenges in building ethical AI and offers solutions to address these issues effectively.

Understanding Ethical AI

Ethical AI refers to the development and deployment of AI systems in a manner that aligns with widely accepted moral principles and societal values. It encompasses several aspects, including fairness, transparency, accountability, privacy, and security. Ethical AI aims to prevent harm and ensure that AI technologies are used to benefit society as a whole.

The Importance of Ethical AI

Trust and Adoption: Ethical AI builds trust among users and stakeholders, encouraging widespread adoption.

Legal Compliance: Adhering to ethical guidelines helps companies comply with regulations and avoid legal repercussions.

Social Responsibility: Developing ethical AI reflects a commitment to social responsibility and the well-being of society.

Challenges in Building Ethical AI

1. Bias and Fairness

AI systems can inadvertently perpetuate or even amplify existing biases present in the training data. This can lead to unfair treatment of individuals based on race, gender, age, or other attributes.

Solutions:

Diverse Data Sets: Use diverse and representative data sets to train AI models.

Bias Detection Tools: Implement tools and techniques to detect and mitigate biases in AI systems.

Regular Audits: Conduct regular audits to ensure AI systems remain fair and unbiased.

2. Transparency and Explainability

AI systems, especially those based on deep learning, can be complex and opaque, making it difficult to understand their decision-making processes.

Solutions:

Explainable AI (XAI): Develop and use explainable AI models that provide clear and understandable insights into how decisions are made.

Documentation: Maintain thorough documentation of AI models, including data sources, algorithms, and decision-making criteria.

User Education: Educate users and stakeholders about how AI systems work and the rationale behind their decisions.

3. Accountability

Determining accountability for AI-driven decisions can be challenging, particularly when multiple entities are involved in developing and deploying AI systems.

Solutions:

Clear Governance: Establish clear governance structures that define roles and responsibilities for AI development and deployment.

Ethical Guidelines: Develop and enforce ethical guidelines and standards for AI development.

Third-Party Audits: Engage third-party auditors to review and assess the ethical compliance of AI systems.

4. Privacy and Security

AI systems often rely on vast amounts of data, raising concerns about privacy and data security.

Solutions:

Data Anonymization: Use data anonymization techniques to protect individual privacy.

Robust Security Measures: Implement robust security measures to safeguard data and AI systems from breaches and attacks.

Consent Management: Ensure that data collection and use comply with consent requirements and privacy regulations.

5. Ethical Design and Implementation

The design and implementation of AI systems should align with ethical principles from the outset, rather than being an afterthought.

Solutions:

Ethical by Design: Incorporate ethical considerations into the design and development process from the beginning.

Interdisciplinary Teams: Form interdisciplinary teams that include ethicists, sociologists, and other experts to guide ethical AI development.

Continuous Monitoring: Continuously monitor AI systems to ensure they adhere to ethical guidelines throughout their lifecycle.

AI Development Companies and Ethical AI

AI development companies play a crucial role in promoting ethical AI. By adopting ethical practices, these companies can lead the way in creating AI technologies that benefit society. Here are some key steps that AI development companies can take to build ethical AI:

Promoting Ethical Culture

Leadership Commitment: Ensure that leadership is committed to ethical AI and sets a positive example for the entire organization.

Employee Training: Provide training on ethical AI practices and the importance of ethical considerations in AI development.

Engaging with Stakeholders

Stakeholder Involvement: Involve stakeholders, including users, in the AI development process to gather diverse perspectives and address ethical concerns.

Feedback Mechanisms: Establish mechanisms for stakeholders to provide feedback and report ethical concerns.

Adopting Ethical Standards

Industry Standards: Adopt and adhere to industry standards and best practices for ethical AI development.

Collaborative Efforts: Collaborate with other organizations, research institutions, and regulatory bodies to advance ethical AI standards and practices.

Conclusion

Building ethical AI is essential for ensuring that AI technologies are used responsibly and for the benefit of society. The challenges in creating ethical AI are significant, but they can be addressed through concerted efforts and collaboration. By focusing on bias and fairness, transparency and explainability, accountability, privacy and security, and ethical design, AI development company can lead the way in developing AI systems that are trustworthy, fair, and beneficial. As AI continues to evolve, ongoing commitment to ethical principles will be crucial in navigating the complex landscape of AI development and deployment.

[familythings]

The Debate Over Autonomous Weapons: Should AI Decide Life or Death?

In the U.S., a heated debate is brewing over the future of autonomous weapons—weapons powered by artificial intelligence (AI) that could potentially decide whether to kill humans without any human input. This issue raises deep moral, ethical, and technological questions. Should we allow machines to make life-or-death decisions? What Are Autonomous Weapons? Autonomous weapons, also known as…

[mehmetyildizmelbourne-blog]

Beware of Cognitive Biases in Generative AI Tools as a Reader, Researcher, or Reporter

Understanding How Human and Algorithmic Biases Shape Artificial Intelligence Outputs and What Users Can Do to Manage Them I have spent over 40 years studying human and machine cognition long before AI reached its current state of remarkable capabilities. Today, AI is leading us into uncharted territories. As a researcher focused on the ethical aspects of technology, I believe it is vital to…

[omegaphilosophia]

Key Differences Between AI and Human Communication: Mechanisms, Intent, and Understanding

The differences between the way an AI communicates and the way a human does are significant, encompassing various aspects such as the underlying mechanisms, intent, adaptability, and the nature of understanding. Here’s a breakdown of key differences:

1. Mechanism of Communication:

AI: AI communication is based on algorithms, data processing, and pattern recognition. AI generates responses by analyzing input data, applying pre-programmed rules, and utilizing machine learning models that have been trained on large datasets. The AI does not understand language in a human sense; instead, it predicts likely responses based on patterns in the data.

Humans: Human communication is deeply rooted in biological, cognitive, and social processes. Humans use language as a tool for expressing thoughts, emotions, intentions, and experiences. Human communication is inherently tied to understanding and meaning-making, involving both conscious and unconscious processes.

2. Intent and Purpose:

AI: AI lacks true intent or purpose. It responds to input based on programming and training data, without any underlying motivation or goal beyond fulfilling the tasks it has been designed for. AI does not have desires, beliefs, or personal experiences that inform its communication.

Humans: Human communication is driven by intent and purpose. People communicate to share ideas, express emotions, seek information, build relationships, and achieve specific goals. Human communication is often nuanced, influenced by context, and shaped by personal experiences and social dynamics.

3. Understanding and Meaning:

AI: AI processes language at a syntactic and statistical level. It can identify patterns, generate coherent responses, and even mimic certain aspects of human communication, but it does not truly understand the meaning of the words it uses. AI lacks consciousness, self-awareness, and the ability to grasp abstract concepts in the way humans do.

Humans: Humans understand language semantically and contextually. They interpret meaning based on personal experience, cultural background, emotional state, and the context of the conversation. Human communication involves deep understanding, empathy, and the ability to infer meaning beyond the literal words spoken.

4. Adaptability and Learning:

AI: AI can adapt its communication style based on data and feedback, but this adaptability is limited to the parameters set by its algorithms and the data it has been trained on. AI can learn from new data, but it does so without understanding the implications of that data in a broader context.

Humans: Humans are highly adaptable communicators. They can adjust their language, tone, and approach based on the situation, the audience, and the emotional dynamics of the interaction. Humans learn not just from direct feedback but also from social and cultural experiences, emotional cues, and abstract reasoning.

5. Creativity and Innovation:

AI: AI can generate creative outputs, such as writing poems or composing music, by recombining existing patterns in novel ways. However, this creativity is constrained by the data it has been trained on and lacks the originality that comes from human creativity, which is often driven by personal experience, intuition, and a desire for expression.

Humans: Human creativity in communication is driven by a complex interplay of emotions, experiences, imagination, and intent. Humans can innovate in language, create new metaphors, and use language to express unique personal and cultural identities. Human creativity is often spontaneous and deeply tied to individual and collective experiences.

6. Emotional Engagement:

AI: AI can simulate emotional engagement by recognizing and responding to emotional cues in language, but it does not experience emotions. Its responses are based on patterns learned from data, without any true emotional understanding or empathy.

Humans: Human communication is inherently emotional. People express and respond to emotions in nuanced ways, using tone, body language, and context to convey feelings. Empathy, sympathy, and emotional intelligence play a crucial role in human communication, allowing for deep connections and understanding between individuals.

7. Contextual Sensitivity:

AI: AI's sensitivity to context is limited by its training data and algorithms. While it can take some context into account (like the previous messages in a conversation), it may struggle with complex or ambiguous situations, especially if they require a deep understanding of cultural, social, or personal nuances.

Humans: Humans are highly sensitive to context, using it to interpret meaning and guide their communication. They can understand subtext, read between the lines, and adjust their communication based on subtle cues like tone, body language, and shared history with the other person.

8. Ethical and Moral Considerations:

AI: AI lacks an inherent sense of ethics or morality. Its communication is governed by the data it has been trained on and the parameters set by its developers. Any ethical considerations in AI communication come from human-designed rules or guidelines, not from an intrinsic understanding of right or wrong.

Humans: Human communication is deeply influenced by ethical and moral considerations. People often weigh the potential impact of their words on others, considering issues like honesty, fairness, and respect. These considerations are shaped by individual values, cultural norms, and societal expectations.

The key differences between AI and human communication lie in the underlying mechanisms, the presence or absence of intent and understanding, and the role of emotions, creativity, and ethics. While AI can simulate certain aspects of human communication, it fundamentally operates in a different way, lacking the consciousness, experience, and meaning-making processes that characterize human interaction.

[techtoio]

AI-Powered Software Solutions: Revolutionizing the Tech World

Introduction

Artificial intelligence has found relevance in nearly all sectors, including technology. AI-based software solutions are revolutionizing innovation, efficiency, and growth like never before in multiple industries. In this paper, we will walk through how AI will change the face of technology, its applications, benefits, challenges, and future trends. Read to continue..

[gothfoxx]

Almost all of Michael Crichton’s books draw on the theme of “you can but that doesn’t mean you should”.

In his iconic book/movie Jurassic Park, Ian Malcolm gives the powerful line of, “Your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should”.

In Prey the organic nanobots are programmed to be a hive-mind with the killing tactics of lions and wolves, the scientists go straight into mass production without testing and end up creating two versions of deadly hive-minds. One parasitic and one predatory.

Next is probably the best example of people not using ethics, animal-human hybrids, companies trying to own gene codes, kidnapping people for their DNA.

Crichton was a man ahead of his time and his lessons remain true, Just because you can doesn’t mean you should.

The AI issue is what happens when you raise generation after generation of people to not respect the arts. This is what happens when a person who wants to major in theatre, or English lit, or any other creative major gets the response, "And what are you going to do with that?" or "Good luck getting a job!"

You get tech bros who think it's easy. They don't know the blood, sweat, and tears that go into a creative endeavor because they were taught to completely disregard that kind of labor. They think they can just code it away.

That's (one of the reasons) why we're in this mess.

[neosciencehub]

Neuralink's First Human Implant

Neuralink's First Human Implant A Leap Towards Human-AI Symbiosis @neosciencehub #neosciencehub #science #Neuralink #Human #AISymbiosis #BrainComputer #Interface #Neurotechnology #elonmusk #AI #brainchip #FutureAI #MedicalTechnology #DataSecurity #NSH

A Leap Towards Human-AI Symbiosis In a landmark achievement that could redefine the boundaries of human potential and technology, Neuralink, the neurotechnology company co-founded by entrepreneur Elon Musk, has successfully implanted its pioneering brain-computer interface in a human subject. This outstanding development in BCI (Brain Computer Interface) not only marks a significant milestone in…

[probablybadrpgideas]

Hasbro: Huh, I notice we still have a tiny amount of Goodwill left among our fanbase.

Hasbro: Time to nip that in the bud!

Hasbro:

[anaquariusfox]

I spent the evening looking into this AI shit and made a wee informative post of the information I found and thought all artists would be interested and maybe help yall?

edit: forgot to mention Glaze and Nightshade to alter/disrupt AI from taking your work into their machines. You can use these and post and it will apparently mess up the AI and it wont take your content into it's machine!

edit: ArtStation is not AI free! So make sure to read that when signing up if you do! (this post is also on twt)

[Image descriptions: A series of infographics titled: “Opt Out AI: [Social Media] and what I found.” The title image shows a drawing of a person holding up a stack of papers where the first says, ‘Terms of Service’ and the rest have logos for various social media sites and are falling onto the floor. Long transcriptions follow.

Instagram/Meta (I have to assume Facebook).

Hard for all users to locate the “opt out” options. The option has been known to move locations.

You have to click the opt out link to submit a request to opt out of the AI scraping. *You have to submit screenshots of your work/face/content you posted to the app, is curretnly being used in AI. If you do not have this, they will deny you.

Users are saying after being rejected, are being “meta blocked”

People’s requests are being accepted but they still have doubts that their content won’t be taken anyways.

Twitter/X

As of August 2023, Twitter’s ToS update:

“Twitter has the right to use any content that users post on its platform to train its AI models, and that users grant Twitter a worldwide, non-exclusive, royalty-free license to do so.”

There isn’t much to say. They’re doing the same thing Instagram is doing (to my understanding) and we can’t even opt out.

Tumblr

They also take your data and content and sell it to AI models.

But you’re in luck!

It is very simply to opt out (Wow. Thank Gods)

Opt out on Desktop: click on your blog > blog settings > scroll til you see visibility options and it’ll be the last option to toggle

Out out of Mobile: click your blog > scroll then click visibility > toggle opt out option

TikTok

I took time skim their ToS and under “How We Use Your Information” and towards the end of the long list: “To train and improve our technology, such as our machine learning models and algorithms.”

Regarding data collected; they will only not sell your data when “where restricted by applicable law”. That is not many countries. You can refuse/disable some cookies by going into settings > ads > turn off targeted ads.

I couldn’t find much in AI besides “our machine learning models” which I think is the same thing.

What to do?

In this age of the internet, it’s scary! But you have options and can pick which are best for you!

Accepting these platforms collection of not only your artwork, but your face! And not only your faces but the faces of those in your photos. Your friends and family. Some of those family members are children! Some of those faces are minors! I shudder to think what darker purposes those faces could be used for.

Opt out where you can! Be mindful and know the content you are posting is at risk of being loaded to AI if unable to opt out.

Fully delete (not archive) your content/accounts with these platforms. I know it takes up to 90 days for instagram to “delete” your information. And even keep it for “legal” purposes like legal prevention.

Use lesser known social media platforms! Some examples are; Signal, Mastodon, Diaspora, et. As well as art platforms: Artfol, Cara, ArtStation, etc.

The last drawing shows the same person as the title saying, ‘I am, by no means, a ToS autistic! So feel free to share any relatable information to these topics via reply or qrt!

I just wanted to share the information I found while searching for my own answers cause I’m sure people have the same questions as me.’ \End description] (thank you @a-captions-blog!)

[aiweirdness]

Among the many downsides of AI-generated art: it's bad at revising. You know, the biggest part of the process when working on commissioned art.

Original "deer in a grocery store" request from chatgpt (which calls on dalle3 for image generation):

revision 5 (trying to give the fawn spots, trying to fix the shadows that were making it appear to hover):

I had it restore its own Jesus fresco.

Original:

Erased the face, asked it to restore the image to as good as when it was first painted:

Wait tumblr makes the image really low-res, let me zoom in on Jesus's face.

Original:

Restored:

One revision later:

Here's the full "restored" face in context:

Every time AI is asked to revise an image, it either wipes it and starts over or makes it more and more of a disaster. People who work with AI-generated imagery have to adapt their creative vision to what comes out of the system - or go in with a mentality that anything that fits the brief is good enough.

I'm not surprised that there are some places looking for cheap filler images that don't mind the problems with AI-generated imagery. But for everyone else I think it's quickly becoming clear that you need a real artist, not a knockoff.

more

[surveillance-capitalism]

It all started with an email James Zou received.

The email was making a request that seemed reasonable, but which Zou realized would be nearly impossible to fulfill.

“Dear Researcher,” the email began. “As you are aware, participants are free to withdraw from the UK Biobank at any time and request that their data no longer be used. Since our last review, some participants involved with Application [REDACTED] have requested that their data should longer be used.”

The email was from the U.K. Biobank, a large-scale database of health and genetic data drawn from 500,000 British residents, thatis widely available to the public and private sector. 

Zou, a professor at Stanford University and prominent biomedical data scientist, had already fed the Biobank’s data to an algorithm and used it to train an A.I. model. Now, the email was requesting the data’s removal. “Here’s where it gets hairy,” Zou explained in a 2019 seminar he gave on the matter. 

That’s because, as it turns out, it’s nearly impossible to remove a user’s data from a trained A.I. model without resetting the model and forfeiting the extensive money and effort put into training it. To use a human analogy, once an A.I. has “seen” something, there is no easy way to tell the model to “forget” what it saw. And deleting the model entirely is also surprisingly difficult.

This represents one of the thorniest, unresolved, challenges of our incipient artificial intelligence era, alongside issues like A.I. “hallucinations” and the difficulties of explaining certain A.I. outputs. According to many experts, the A.I. unlearning problem is on a collision course with inadequate regulations around privacy and misinformation: As A.I. models get larger and hoover up ever more data, without solutions to delete data from a model — and potentially delete the model itself — the people affected won’t just be those who have participated in a health study, it’ll be a salient problem for everyone. 

Why A.I. models are as difficult to kill as a zombie

In the years since Zou’s initial predicament, the excitement over generative A.I. tools like ChatGPT has caused a boom in the creation and proliferation of A.I. models. What’s more, those models are getting bigger, meaning they ingest more data during their training.

Many of these models are being put to work in industries like medical care and finance where it’s especially important to be careful about data privacy and data usage.

But as Zou discovered when he set out to find a solution to removing data, there’s no simple way to do it. That’s because an A.I. model isn’t just lines of coding. It’s a learned set of statistical relations between points in a particular dataset, encompassing subtle relationships that are often far too complex for human understanding. Once the model learns this relationship, there’s no simple way to get the model to ignore some portion of what it has learned.

“If a machine learning-based system has been trained on data, the only way to retroactively remove a portion of that data is by re-training the algorithms from scratch,” Anasse Bari, an A.I. expert and computer science professor at New York University, told Fortune.

The problem goes beyond private data. If an A.I. model is discovered to have gleaned biased or toxic data, say from racist social media posts, weeding out the bad data will be tricky.

Training or retraining an A.I. model is expensive. This is particularly true for the ultra-large “foundation models” that are currently powering the boom in generative A.I. Sam Altman, the CEO of OpenAI, has reportedly said that GPT-4, the large language model that powers its premium version of ChatGPT, cost in excess of $100 million to train.

That’s why, to companies developing A.I. models, a powerful tool that the U.S. Federal Trade Commission has to punish companies it finds have violated U.S. trade laws is scary. The tool is called “algorithmic disgorgement.” It’s a legal process that penalizes the law-breaking company by forcing it to delete an offending A.I. model in its entirety. The FTC has only used that power a handful of times, typically directed at companies who have misused data. One well known case where the FTC did use this power is against a company called Everalbum, which trained a facial recognition system using people’s biometric data without their permission.

But Bari says that algorithmic disgorgement assumes those creating A.I. systems can even identify which part of a dataset was illegally collected, which is sometimes not the case. Data easily traverses various internet locations, and is increasingly “scraped” from its original source without permission, making it challenging to determine its original ownership.

Another problem with algorithmic disgorgement is that, in practice, A.I. models can be as difficult to kill as zombies. 

“Trying to delete an AI model might seem exceedingly simple, namely just press a delete button and the matter is entirely concluded, but that’s not how things work in the real world,” Lance Elliot, an A.I. expert, told Fortune in an email. 

A.I. models can be easily reinstated after deletion because it’s likely other digital copies of the model exist and can be easily reinstated, Elliot writes.

Zou says that, the way things stand, either the technology needs to change substantially so that companies can comply with the law, or lawmakers need to rethink the regulations and how they can make companies comply.

Building smaller models is good for privacy

In his research, Zou and his collaborators did come up with some ways that data can be deleted from simple machine learning models that are based on a technique known as clustering without compromising the entire model. But those same methods won’t work for more complex models such as most of the deep learning systems that underpin today’s generative A.I. boom. For these models, a different kind of training regime may have to be used in the first place to make it possible to delete certain statistical pathways in the model without compromising the whole model’s performance or requiring the entire model to be retrained, Zou and his co-authors suggested in a 2019 research paper.

For companies worried about the requirement that they be able to delete users data upon request, which is a part of several European data privacy laws, other methods may be needed. In fact, there’s at least one A.I. company that has built its entire business around this idea. 

Xayn is a German company that makes private, personalized A.I. search and recommendation technology. Xayn’s technology works by using a base model and then training a separate small model for each user. That makes it very easy to delete any of these individual users’ models upon request.

“This problem of your data floating into the big model never happens with us,” Leif-Nissen Lundbæk, the CEO and co-founder of Xayn, said. 

Lundbæk said he thinks Xayn’s small, individual A.I. models represent a more viable way to create A.I. in a way that can comply with data privacy requirements than the massive large language models being built by companies such as OpenAI, Google, Anthropic, Inflection, and others. Those models suck up vast amounts of data from the internet, including personal information—so much that the companies themselves often have poor insight into exactly what data is contained in the training set. And these massive models are extremely expensive to train and maintain, Lundbaek said. 

Privacy and artificial intelligence businesses are currently a sort of parallel development, he said. 

Another A.I. company trying to bridge the gap between privacy and A.I. is SpotLab, which builds models for clinical research. Its founder and CEO Miguel Luengo-Oroz previously worked at the United Nations as a researcher and chief data scientist. In 20 years of studying A.I., he says he  has often thought about this missing piece: an A.I.’s system’s ability to unlearn.

He says that one reason little progress has been made on the issue is that, until recently, there was no data privacy regulation forcing companies and researchers to expend serious effort to address it. That has changed recently in Europe, but in the U.S., rules that would require companies to make it easy to delete people’s data are still absent.

Some people are hoping the courts will step in where lawmakers have so far failed. One recent lawsuit alleges OpenAI stole “millions of Americans'” data to train ChatGPT’s model.

And there are signs that some big tech companies may be starting to think harder about the problem. In June, Google announced a competition for researchers to come up with solutions to A.I.’s inability to forget.

But until more progress is made, user data will continue to float around in an expanding constellation of A.I models, leaving it vulnerable to dubious, or even threatening, actions.

“I think it’s dangerous and if someone got access to this data, let’s say, some kind of intelligence agencies or even other countries, I mean, I think it can be really be used in a bad way,” Lundbæk said.

[getmoneymethods]

Ethics of AI: Navigating Challenges of Artificial Intelligence

Introduction: Understanding the Ethics of AI in the Modern World Artificial Intelligence (AI) has become an integral part of our lives, revolutionizing various industries and transforming the way we live and work. From virtual assistants to self-driving cars, AI technologies have brought about unprecedented advancements. However, with these advancements come ethical challenges that need to be…

[twelveskidneys]

steven moffat really said alright how many aspects of modern society can i criticise in the one (1) episode i’m writing for this season

[nicholasandriani]

From Wired to Worried: A Review of the Article on AI's Potential Threats

As a curious technologist and systems scholar, I was intrigued by the recent article published by WILL KNIGHT and PARESH DAVE titled “In Sudden Alarm, Tech Doyens Call for a Pause on ChatGPT.” The article discussed the concerns of tech luminaries, renowned scientists, and even Elon Musk himself about the potential dangers of the development and deployment of increasingly powerful AI systems, like…