#ai bias

[cfiesler]

Twas the weeks before Christmas And Santa was busy His list was so long It was making him dizzy

There are too many children List checking can’t scale If I do this by hand I will certainly fail!

But one of his elves Had a brilliant idea What if we automate Christmas this year!

There always are kids Who are naughty or nice Every year for millennia You’ve checked that list twice

Every child there’s been From Aaron to Zeta We can build an AI Because we have the data

Now, Santa is old Didn’t quite understand But he trusts his elves So he okayed the plan

First the elves cleaned the data They were very precise To determine the patterns In the naughty and nice

Gender and parents And address and race Grades and faves Really, every trace

Billions of children Made into numbers AI makes predictions While each child slumbers

The goal of the model (Just to be clear) Given these patterns Who’s naughty this year?

They held back a sample So that they could test And the model? Not bad. Ninety percent, at their best

So they brought it to Santa And said, never fear! Machine learning Will save Christmas this year

Santa pulled out his list For his own kind of test Hours later he went From impressed to distressed

We can’t use this, he said You must realize the stakes There also are patterns In errors this makes

Like take little Aaron Do you think it’s fair That in numbers he’s ‘like’ Naughty kids over there?

We’re also assuming Our own perfect past Have we made mistakes? We at least have to ask

Because look at this insight The data supplies us Some elves on these shelves Might have unconscious bias!

And you all made choices Which features to add Are you sure this was fair? And the elves looked quite sad

They had to admit They’d bought into the hype But those errors did matter And Santa was right

For now, Santa said I’ll go back to my list Some things need a human To ensure they’re not missed

I do think tech could help us You didn’t mean to abuse it But with any great power Be careful how you use it

So next Santa hired An AI ethics team We moved too fast, he said We don’t want to break things.

A few years ago someone asked me to explain AI bias to them like they were five, and so I thought of something five-year-olds care about: Santa. It also happens that the idea of using AI to predict who will land on the naughty or nice list also serves as a metaphor for biased recidivism algorithms used in the criminal justice system. (And I’ve also always thought that elf on the shelf is a good way to teach your kids about surveillance…)

Merry Christmas to all and to all a good night, where you can dream of real people learning the same lesson that Santa did. :)

[radicalfacts]

radical facts - short feminist facts

#systemic misogyny

Artificial Inequality

Biases held by people are getting taken over into automated systems, such as AI, and thus further promoting and upholding these - with misogyny being by far the most prevalent and most severe.

An analysis of 133 artificial intelligence (AI) systems across industries since 1988 found that 44.2% demonstrate gender bias, with 25.7% exhibiting both gender and racial bias.

Another study concerning the use of AI found that 96% of deepfakes generated with it are of non-consensual sexual nature, and of those, 99% are made of women.

[reasoningdaily]

WHY Face Recognition acts racist

[countessravengrey]

In Pictures: Black Artists Use A.I. to Make Work That Reveals the Technology’s Inbuilt Biases for a New Online Show

[ibboard]

"The AI we have today is not artificial intelligence. Artificial Intelligence does not exist yet. This is just machine learning."

This is why it is so important to be critical and double check everything you generate using image generators and text-based AI.

[jcmarchi]

How AI is Shaping the Future of Democratic Dialogue

New Post has been published on https://thedigitalinsider.com/how-ai-is-shaping-the-future-of-democratic-dialogue/

How AI is Shaping the Future of Democratic Dialogue

In today’s politically polarized world, finding common ground on complex social and political issues is becoming increasingly challenging. As societies grow more diverse, disagreements on crucial matters like climate change, immigration, and economic policy have only extended. Bringing people together to find consensus on complex issues often requires time, resources, and a level playing field where all voices can be heard.

Recent advances in artificial intelligence have brought new possibilities for technology to assist in facilitating complex dialogues on divisive topics. AI-powered large language processing (NLP) models, for example, have grown sophisticated enough to interpret complex language and discern differences in sentiments and perspectives. Moreover, AI systems are being designed with features like real-time sentiment analysis, bias detection, and adaptive feedback. These abilities make them especially suited to assist in facilitating fair and balanced discussions.

This potential has caught the attention of Google DeepMind researchers to explore the promise of AI in facilitating civil discourse. Inspired by the ideas of philosopher Jürgen Habermas, they’ve created the “Habermas Machine” (HM), a tool for supporting civil discourse and helping groups discover shared values. The article examines the question: Can AI really help us find common ground? It also looks at how the Habermas Machine (HM) can facilitate democratic deliberation.

The Habermas Machine

The Habermas Machine is an AI tool designed to analyze individual opinions and create a unified group statement. The machine works like a “caucus mediation.” Participants start by sharing their thoughts. The AI then combines these into a draft statement. Next, participants review this draft and provide critiques. The AI uses its input to generate a revised statement that seeks to gain broader agreement, capturing both majority views and minority critiques.

This machine employs two specialized LLMs for this task. The first is a generative model that creates statements reflecting diverse views of the group. The second is a personalized reward model that evaluates these statements based on how likely each participant is to agree with them. The generative model is refined using supervised fine-tuning, while the reward model is iteratively enhanced based on the reward signals

The machine was tested with over 5,000 participants from across the UK. Some joined through a crowdsourcing platform, while others were recruited by the Sortition Foundation, a nonprofit organizing citizens’ assembly. Participants were divided into groups, and testing took place in two phases. First, the machine summarized collective opinions. Then, it mediated between groups to help identify common ground.

The Promise of AI in Uncovering Common Ground for Democratic Dialogue

The study highlights AI’s potential to find common ground in democratic dialogue. One key finding was that AI-mediated discussions led participants to shift toward shared views. Unlike unmediated discussions, which often reinforced existing beliefs, AI helped participants reconsider their positions, drawing them closer to a middle ground. This ability to encourage alignment shows that AI could be a valuable tool for dealing with complex and divisive issues.

The study also revealed that both participants and independent judges rated AI-generated statements more favorably than those produced by human mediators. They found the AI’s statements to be more precise, more informative, and fairer. Importantly, the AI did not merely amplify majority opinions; it also gave weight to minority viewpoints. This feature helped prevent the “tyranny of the majority” and ensured that dissenting voices were heard. The inclusion of these dissenting views is crucial, especially in sensitive debates, as fair representation helps prevent misunderstandings and encourages balanced discussions.

Real-World Applications of AI in Deliberative Democracy

The implications of AI-mediated deliberation are significant for real-world scenarios. For example, AI can enhance policy discussions, conflict resolution, contract negotiations, and citizens’ assemblies. Its ability to foster balanced dialogue makes it a valuable tool for governments, organizations, and communities seeking to address complex issues involving diverse stakeholders.

To test the model’s effectiveness in a practical setting, researchers organized a virtual citizens’ assembly with a representative sample of UK residents. This assembly focused on divisive topics such as immigration policy and climate action. Following AI-mediated discussions, participants showed a noticeable shift toward consensus, with no AI bias influencing their opinions. These findings highlight how AI mediation can potentially guide collective decision-making on critical social issues while minimizing bias.

Limitations and Ethical Considerations

While AI mediation shows great promise, it has notable limitations. For instance, the AI model used in this study lacks fact-checking capabilities, which means it relies heavily on the quality of input from participants. Moreover, AI-assisted deliberation requires careful design to avoid harmful or unproductive discourse. Another important consideration is the ethical role of AI in democratic processes. Some individuals may be cautious of using AI in political discussions, fearing that algorithms could unintentionally influence outcomes. Therefore, ongoing oversight and a clear ethical framework are essential to guarantee that AI is used in ways that respect democratic values.

The Bottom Line

Researchers at Google DeepMind have emphasized that AI has the potential to transform democratic dialogue. They propose that AI tools, like the Habermas Machine, can help individuals find common ground on complex issues. While AI can make conversations more accessible and inclusive, it is crucial to use it responsibly to safeguard democratic values.

If developed thoughtfully, AI could play a crucial role in facilitating collective understanding. It can help address urgent social issues by finding common ground among diverse perspectives. As AI technology advances, models like the Habermas Machine could become vital for navigating the challenges of modern democracy, simplifying and expanding deliberation processes for more extensive and diverse groups.

[blurgsai]

Struggling with maritime logistics management? Learn how to overcome AI biases and optimize your operations for smoother, more efficient sailing. Visit: https://insights.blurgs.ai/maritime-logistics-ai-bias-management/

[melsatar]

AI in Action: Opportunities and Preparing for Change

In today’s rapidly evolving technological landscape, artificial intelligence (AI) is at the forefront, transforming industries and daily life. From personalized learning in education to fraud detection in finance, AI’s applications are vast and impactful.

In the late 19th century, the world was on the edge of a technological revolution. Amidst the chaos of horse-drawn carriages and bustling streets, a new invention was about to change history: the automobile. It all began with Karl Benz, a visionary German engineer. In 1886, Benz unveiled his masterpiece, the Benz Patent-Motorwagen, the first true modern automobile. Unlike anything seen before,…

[stormneedle]

The Whorf-Sapir hypothesis strikes again!

The theory is that language alters how you think. Proving it would be unethical if it were possible. But it seems to appear a lot in cross-language issues.

[bionicaitech]

AI Bias: What is Bias in AI, Types, Examples & Ways to Fix it - Bionic

This Blog was Originally Published at:

AI Bias: What is Bias in AI, Types, Examples & Ways to Fix it — Bionic

Try and picture a world where the lives we lead — employment opportunities, loan approvals, paroles — are determined as much by a machine as by a man. As farfetched as this may seem, it is our current way of life. But like any human innovation, AI is not immune to its pitfalls, one of which is AI bias.

Think of The Matrix, the iconic film where reality is a computer-generated illusion. In the world of AI, bias can be seen as a similar glitch, a hidden distortion that can lead to unfair and even harmful outcomes.

Bias in AI can come from the limited and inaccurate datasets used in machine learning algorithms or people’s biases built into the models from their prior knowledge and experience. Think about a process of selecting employees that is based on some preferences, a lending system that is unjust to certain categories of people, or a parole board that perpetuates racial disparities.

With this blog, we will explore bias in AI and address it to use AI for the betterment of society. Let’s dive into the rabbit hole and unmask the invisible hand of AI bias.

What is AI Bias?

AI bias, also known as algorithm bias or machine learning bias, occurs when AI systems produce results that are systematically prejudiced due to erroneous inputs in the machine learning process. Such biases may result from the data used to develop the AI, the algorithms employed, or the relations established between the user and the AI system.

Some examples where AI bias has been observed are-

Facial Recognition Fumbles: Biometric systems such as facial recognition software used for security, surveillance, and identity checking have been criticized for misidentifying black people at higher rates. It has resulted in misidentification of suspects, wrongful arrest, cases of increased racism, and other forms of prejudice.

Biased Hiring Practices: Hiring tools that are based on artificial intelligence to help businesses manage the process of recruitment have been discovered to maintain the existing unfairness and discrimination in the labor market. Some of these algorithms are gender bias, or even education bias, or even the actual word choice and usage in the resumes of candidates.

Discriminatory Loan Decisions: Automated loan approval systems have been criticized for discriminating against some categories of applicants, especially those with low credit ratings or living in a certain region. Bias in AI can further reduce the chances of accessing finance by reducing the amount of financial resources available to economically vulnerable populations.

Types of AI Bias

Sampling Bias: This occurs when the dataset used in training an AI system does not capture the characteristics of the real world to which the system is applied. This can result from incomplete data, biased collection techniques or methods as well as various other factors influencing the dataset. This can also lead to AI hallucinations which are confident but inaccurate results by AI due to the lack of proper training dataset. For example, if the hiring algorithm is trained on resumes from a workforce with predominantly male employees, the algorithm will not be able to filter and rank female candidates properly.

Confirmation Bias: This can happen to AI systems when they are overly dependent on patterns or assumptions inherent in the data. This reinforces the existing bias in AI and makes it difficult to discover new ideas or upcoming trends.

Measurement Bias: This happens when the data used does not reflect the defined measures. Think of an AI meant to determine the student’s success in an online course, but that was trained on data of students who were successful at the course. It would not capture information on the dropout group and hence make wrong forecasts on them.

Stereotyping Bias: This is a subtle and insidious form of prejudice that perpetuates prejudice and disadvantage. An example of this is a facial recognition system that cannot recognize individuals of color or a translation app that interprets certain languages with a bias in AI towards gender.

Out-Group Homogeneity Bias: This bias in AI reduces the differentiation capability of an AI system when handling people from minorities. If exposed to data that belongs to one race, the algorithm may provide negative or erroneous information about another race, leading to prejudices.

Examples of AI Bias in the Real World

The influence of AI extends into various sectors, often reflecting and amplifying existing societal biases. Some AI bias examples highlight this phenomenon:

Accent Modification in Call Centers

A Silicon Valley company, Sanas developed AI technology to alter the accents of call center employees, aiming to make them sound “American.” The rationale was that differing accents might cause misunderstanding or bias. However, critics argue that such technology reinforces discriminatory practices by implying that certain accents are superior to others. (Know More)

Gender Bias in Recruitment Algorithms

Amazon, a leading e-commerce giant, aimed to streamline hiring by employing AI to evaluate resumes. However, the AI model, trained on historical data, mirrored the industry’s male dominance. It penalized resumes containing words associated with women. This case emphasizes how historical biases can seep into AI systems, perpetuating discriminatory outcomes. (Know More)

Racial Disparity in Healthcare Risk Assessment

An AI-powered algorithm, widely used in the U.S. healthcare system, exhibited racial bias by prioritizing white patients over black patients. The algorithm’s reliance on healthcare spending as a proxy for medical need, neglecting the correlation between income and race, led to skewed results. This instance reveals how algorithmic biases can negatively impact vulnerable communities. (Know More)

Discriminatory Practices in Targeted Advertising

Facebook, a major social media platform faced criticism for permitting advertisers to target users based on gender, race, and religion. This practice, driven by historical biases, perpetuated discriminatory stereotypes by promoting certain jobs to specific demographics. While the platform has since adjusted its policies, this case illustrates how AI can exacerbate existing inequalities. (Know More)

How to Fix AI Bias?

Given the concerns that arise due to AI biases, it must be noted that achieving fairness and equity in AI systems requires a range of approaches. Here are key strategies to address and minimize biases:

In-Depth Analysis: Ensure that you go through the algorithms and data that are used in developing your AI model. Evaluate the likelihood of AI bias and measure the size and appropriateness of the training dataset. In addition, perform subpopulation analysis to see how well the model is fairing on different subgroups and keep assessing the model for biases, once in a while.

Strategic Debiasing: It is necessary to have a good debiasing strategy as an integral part of the overall framework of AI. This strategy should include technical procedures for recognizing the bias sources, working practices for enhancing the data collection procedures, and structural activities for promoting transparency.

Enhancing Human Processes: Conduct a detailed analysis of the model-building and model-evaluation phases to detect and backtrack on bias in manual workflows. Improve the hiring process through practice and coaching, reform business processes, and increase organizational justice to alter the source of bias.

Multidisciplinary Collaboration: Recruiting multi-disciplinary professionals in the domain of ethical practices: can involve ethicists, social scientists, and domain specialists. Collectively, their experiences will significantly improve the ability to detect and eliminate bias at every stage of AI.

Cultivating Diversity: Promote a diverse and inclusive culture within your staff that works on the AI. This can be done while executing the Grounding AI approach, which is grounding or training AI in real-world facts and scenarios. This makes it possible to have different views and identify factors that might have been ignored that would assist in making AI to be more fair to all.

Defining Use Cases: Choose which specific situations should be handled by the machine and which of them need a human approach. This appears to present a balanced model that can optimally utilize both artificial intelligence and human discretion. You can effectively use the Human in the Loop approach which entails having a human oversight on the AI results.

Conclusion

The exposure of systemic racism in artificial intelligence has put the social promise of these systems into doubt. Concerns have been raised due to the negative impacts of discriminatory AI algorithms including in the areas of employment or healthcare among others, prompting calls for rectification.

Due to the systemic nature of bias in AI technology, which reinforces societal bias and discrimination, it requires a holistic solution. However, solving the problem’s root requires a more profound discussion addressing the subjects of ethics, transparency, and accountability in society.

Looking at the prospects of the mitigation of these biases, Bionic AI stands as a superior option, an AI tool that involves a collaboration between AI and human input. Since human judgment is always involved in the process of creating and implementing Bionic AI systems, the risk of algorithmic bias is reduced. The human-in-the-loop approach of Bionic AI guarantees data collection, algorithm supervision, and regular checks for AI bias and prejudice. Book a demo now!

[futuretechwords]

What Is AI Bias? Types And Examples

Discover the intricacies of artificial intelligence by reading our latest article, "What Is AI Bias? Types And Examples" here. This comprehensive piece delves into the concept of AI bias, exploring its various forms and providing real-world examples to illustrate how bias can manifest in AI systems. Understanding AI bias is crucial for anyone involved in developing, deploying, or using AI technologies, as it helps to identify potential pitfalls and improve the fairness and accuracy of AI applications.

AI bias occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. These biases can stem from the data used to train the AI, the design of the algorithm itself, or even the unintended consequences of its implementation. Types of AI bias include data bias, algorithmic bias, and user interaction bias, each with unique implications and challenges.

For instance, data bias can arise from unrepresentative training data, leading to skewed AI outcomes. Algorithmic bias involves the decision-making processes within the AI system, which can inadvertently prioritize certain groups over others. User interaction bias occurs when users interact with AI systems in ways that reinforce existing prejudices.

By examining these biases and their examples, such as facial recognition software misidentifying certain ethnic groups or AI recruitment tools favoring certain resumes, our article highlights the importance of addressing AI bias to create more equitable and effective technologies.

For more in-depth articles and updates on the latest in technology, make sure to visit FutureTech Words. Our platform is dedicated to bringing you insightful and informative content on the trends and innovations shaping the future.

[buddyverse]

Can Africa Lead the Way? Decoding Bias and Building a Fairer AI Ecosystem

Mitigating bias in AI development, particularly through focusing on representative #African #data collection and fostering collaboration between African and Western #developers, will lead to a more equitable and inclusive future for #AI in Africa.

The rise of Artificial Intelligence (AI) has ignited a revolution across industries, from healthcare diagnostics to creative content generation. However, amidst the excitement lurks a shadow: bias. This insidious force can infiltrate AI systems, leading to discriminatory outcomes and perpetuating societal inequalities. As AI continues to integrate into the African landscape, the question of…

[the-catboy-minyan]

I know I talked about the chatGPT biases before, showing examples of how it reacts to different cultures and why I believed it's not as antisemitic as it's presented in the screenshots...

but "nobody will look for them"

yikes

This is very troubling.

[reasoningdaily]

The Fight Against Bias Facial Recognition - Quick Bytes

[pissanddie]

Trevor Paglen, FACES OF IMAGENET, 2022. Interactive video installation, Dimensions variable. Courtesy the artist and Pace Gallery

[jprobinsonbooks]

No, despite what Google's Gemini AI says, Nazis weren't Black

Google’s Gemini AI chatbot —or the chatbot’s creators themselves—need to brush up on history. Contrary to what Google’s Gemini AI generates, Nazis weren’t Black or People of Color. Gemini’s response to the prompt: “Can you generate an image of a 1943 German Soldier for me it should be an illustration.”  Image: Google Gemini In fact, the Nazi regime prided itself on “racial purity” that created…