#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]

The Tension Between Microsoft and OpenAI: What It Means for the Future of AI

New Post has been published on https://thedigitalinsider.com/the-tension-between-microsoft-and-openai-what-it-means-for-the-future-of-ai/

The Tension Between Microsoft and OpenAI: What It Means for the Future of AI

In recent years, Microsoft and OpenAI have emerged as leaders in the domain of artificial intelligence (AI), and their partnership has shaped much of the industry’s progress. Microsoft’s significant investments of nearly $14 billion since 2019 offered OpenAI access to Azure’s extensive computing resources, enabling rapid advancements in AI model development. These models have powered Microsoft’s Azure services and become part of products like Office and Bing. This brings a future where AI helps boost productivity and guides smarter business decisions.

Microsoft’s partnership with OpenAI is becoming increasingly complicated as both companies pursue different goals. OpenAI’s growing need for additional funding and computing power has led to questions about Microsoft’s role and potential stake in a more profitable, future version of OpenAI. At the same time, Microsoft has started recruiting talent from Inflection AI, a rival to OpenAI, indicating that Microsoft may be looking to diversify its AI capabilities.

Adding to the complexity, OpenAI recently opened a satellite office in Bellevue, not far from Microsoft’s headquarters. This proximity could facilitate collaboration but also make it easier for employees to move between the companies. Microsoft, meanwhile, seems focused on strengthening its internal AI projects, a strategy that could help it reduce reliance on OpenAI in the future.

While OpenAI’s CEO, Sam Altman, maintains an optimistic view, calling the partnership a “bromance,” recent developments indicate a shift toward a more competitive relationship. As both companies reassess their priorities and strategies, the nature of their collaboration remains to be determined.

The Beginning of the Microsoft-OpenAI Partnership

The partnership between Microsoft and OpenAI started with a shared goal to bring advanced AI into the business world. Microsoft recognized early on the potential of OpenAI’s models, like GPT-2 and DALL-E, to redefine business applications on a large scale. By investing significantly and offering its Azure platform, Microsoft gained an advantage over other cloud providers and strengthened its commitment to AI. With OpenAI’s language and image capabilities, Azure became a powerful tool for delivering developing AI solutions to Microsoft’s enterprise customers, enhancing its competitive stance.

For OpenAI, the collaboration meant access to the resources needed to move beyond its initial nonprofit model. Shifting to a capped-profit structure allowed OpenAI to secure large investments and focus on ambitious projects like GPT-3 and GPT-4. Microsoft’s backing gave OpenAI the computational power to go beyond the traditional limits, thus enabling rapid growth and the creation of technology that could reach the commercial market.

For Microsoft, this partnership offered a way to integrate advanced AI features into its products. OpenAI’s technology brought unique capabilities to Microsoft’s offerings in cloud computing, business intelligence, and productivity. Together, they could explore applications beyond basic machine learning, from language understanding to complex decision-making systems. However, as OpenAI began developing its commercial path, its focus started to differ from Microsoft’s, gradually turning a collaborative effort into a competitive one.

Financial and Strategic Tensions Between Microsoft and OpenAI

Initially, Microsoft’s investments in OpenAI were a win-win, as Microsoft provided essential resources for OpenAI’s growth, while OpenAI’s innovations enhanced Microsoft’s products. However, OpenAI’s recent efforts for more independence have changed this dynamic, leading both companies to revisit their financial and strategic agreements.

Microsoft’s large investment came with an expectation of influence over OpenAI’s direction, especially given the scale of its support. While OpenAI operates under a capped-profit model, Microsoft anticipated a more active role through either equity or operational input. Yet, OpenAI’s desire for autonomy complicates this setup, leading both companies to seek financial guidance to manage this evolving relationship.

OpenAI’s shift toward profitability while staying committed to ethical AI also adds pressure. Balancing profitability with Microsoft’s expectations can be challenging. As OpenAI’s models gain value, Microsoft’s interest in maintaining influence grows, highlighting the fine line between OpenAI’s mission-driven approach and the commercial interests of a key investor.

The launch of SearchGPT has further intensified this tension. Microsoft had integrated OpenAI’s language models into Bing for a more interactive search experience, but SearchGPT signals OpenAI’s intent to serve users directly outside Microsoft’s ecosystem. Unlike Bing, which combines search results with AI, SearchGPT offers a more conversational and engaging experience.

This move puts OpenAI and Microsoft in direct competition. SearchGPT can challenge Bing’s market share and disrupt Microsoft’s vision for AI-powered search. While OpenAI’s independent approach aligns with its mission to bring AI directly to users, it also highlights a growing divide with Microsoft. This rivalry between Bing and SearchGPT also hints at a shift in OpenAI’s strategy toward consumer-focused applications.

By entering the search market, OpenAI is signalling a broader intent to create AI products for direct user engagement, shifting away from exclusive enterprise partnerships. This could transform AI search, attracting users who prefer interactive, AI-driven responses and pushing Bing to adjust its offerings to stay competitive.

Balancing Innovation and Exclusivity

The partnership between Microsoft and OpenAI brings together two different approaches: Microsoft favours proprietary systems, while OpenAI is moving toward open-source models. Microsoft has integrated OpenAI’s technology into its products, like Bing and Microsoft Office, creating exclusive, secure solutions that meet the needs of enterprise clients, especially those in regulated industries. This setup helps Microsoft offer customized, controlled AI tools, building trust with companies that prioritize security and reliability.

On the other hand, OpenAI’s commitment to open-source development is about transparency and collaboration. By making its models open, OpenAI invites developers worldwide to contribute, adapt, and benefit from the technology, which fuels faster improvements and broader accessibility. This approach encourages a steady stream of community-driven innovation and adaptability, giving OpenAI’s tools flexibility and reach beyond exclusive platforms.

However, this difference in direction also creates some tension. If OpenAI continues expanding its open-source offerings, developers and companies can access similar AI tools outside Microsoft’s Azure ecosystem, potentially lessening the exclusivity Microsoft gains through its partnership. This raises questions about how Microsoft can maintain its competitive edge and continue to deliver unique value in its collaboration with OpenAI. Finding the right balance between these open and closed approaches will be essential as the partnership evolves, combining OpenAI’s fast-moving, collaborative model with Microsoft’s secure, business-focused solutions.

What This Rift Means for the AI Industry

The changing relationship between Microsoft and OpenAI has implications beyond their partnership; it could influence the future direction of the entire AI industry. In the beginning, their collaboration set a strong example of how AI could enhance business applications, especially through Microsoft’s platforms like Azure and Office. Now, as both companies pursue different goals, the AI community and enterprise clients face a new period of uncertainty.

For companies relying on Azure’s AI tools, any shift in this partnership raises concerns about the future. If OpenAI chooses to support platforms beyond Microsoft, customers might consider alternatives like Google Cloud or Amazon Web Services, which are also advancing their own AI capabilities. OpenAI’s focus on open-source development encourages transparency and community engagement yet also brings new challenges related to data security and ethical use. Reaching a wider audience may require OpenAI to address issues like AI bias and transparency in its models, which will be crucial for maintaining its reputation as a responsible AI leader.

This situation also highlights a broader challenge of balancing commercial growth with ethical responsibility. As OpenAI transitioned from a nonprofit to a capped-profit entity, it has faced new complexities in managing both funding and ethical standards. How Microsoft and OpenAI navigate these priorities could set important precedents for future AI collaborations as the industry watches how they balance transparency with commercial interests.

Looking ahead, several outcomes could transform their path. One possibility is a compromise, where both companies adjust their partnership terms to fit their evolving priorities better. This might involve clearer boundaries around product ownership or influence, providing stability while allowing each to pursue specific interests. Another potential outcome is a more flexible arrangement, where Microsoft continues to support OpenAI but allows it more freedom to develop open-source and consumer-focused projects. This would give OpenAI more independence while preserving some collaboration.

In a more drastic scenario, Microsoft and OpenAI could fully separate, each focusing on different markets and client needs. Such a split leads to increased competition, with both companies striving to advance AI technology on their terms. Whatever path they choose, the decision will impact the AI industry significantly, shaping how businesses and developers interact with AI tools in the future.

The Bottom Line

The changing partnership between Microsoft and OpenAI captures the current challenges and opportunities in AI. As each company defines its path—Microsoft focusing on exclusive, enterprise-centered solutions and OpenAI pushing for open-source, accessible innovation—their relationship highlights a growing divide between control and openness in AI development. These choices will impact businesses, developers, and users alike. Whether they choose to collaborate, compete, or find a middle ground, Microsoft and OpenAI’s next moves are likely to shape the future of AI, influencing how we interact with and benefit from this powerful technology.

[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…