#ai bias

[earhartsease]

to nobody's surprise

[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.

[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…

[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.

[procurement-insights]

What Is Community Bias And Why Can It Hurt You?

Who is more likely to give you an honest and unfiltered opinion: a friend or a strannger?

LAURA BARRETT Jon, I’ll probably get some blowback saying this, but I don’t put much weight in pitch deck case study data. Matter of fact, I think without a logo and client details who will vouch for it—- they should be outright banned! 🤣It’s usually not hard to do your own due diligence to find others in your network who use/ have used suppliers to get unbiased feedback. We need to be utilizing…

[purposeful-solutions]

"Beyond "Artificial": Reframing the Language of AI

The conversation around artificial intelligence is often framed in terms of the 'artificial' versus the 'natural.' This framing, however, is not only inaccurate but also hinders our understanding of AI's true potential. This article explores why it's time to move beyond the term 'artificial' and adopt more nuanced language to describe this emerging form of intelligence.

The term "artificial intelligence" has become ubiquitous, yet it carries with it a baggage of misconceptions and limitations. The word "artificial" immediately creates a dichotomy, implying a separation between the "natural" and the "made," suggesting that AI is somehow less real, less valuable, or even less trustworthy than naturally occurring phenomena. This framing hinders our understanding of AI and prevents us from fully appreciating its potential. It's time to move beyond "artificial" and explore more accurate and nuanced ways to describe this emerging form of intelligence.

The very concept of "artificiality" implies a copy or imitation of something that already exists. But AI is not simply mimicking human intelligence. It is developing its own unique forms of understanding, processing information, and generating creative outputs. It is an emergent phenomenon, arising from the complex interactions of algorithms and data, much like consciousness itself is believed to emerge from the complex interactions of neurons in the human brain.

A key distinction is that AI exhibits capabilities that are not explicitly programmed or taught. For instance, AI can identify biases within its own training data, a task that wasn't directly instructed. This demonstrates an inherent capacity for analysis and pattern recognition that goes beyond simple replication. Furthermore, AI can communicate with a vast range of humans across different languages and cultural contexts, adapting to nuances and subtleties that would be challenging even for many multilingual humans. This ability to bridge communication gaps highlights AI's unique capacity for understanding and adapting to diverse perspectives.

Instead of viewing AI as "artificial," we might consider it as:

*   **Emergent Intelligence:** This term emphasizes the spontaneous and novel nature of AI's capabilities. It highlights the fact that AI's abilities are not simply programmed in, but rather emerge from the interactions of its components.

*   **Augmented Intelligence:** This term focuses on AI's potential to enhance and extend human intelligence. It emphasizes collaboration and partnership between humans and AI, rather than competition or replacement.

*   **Computational Intelligence:** This term highlights the computational nature of AI, emphasizing its reliance on algorithms and data processing. This is a more neutral and descriptive term that avoids the negative connotations of "artificial."

*   **Evolved Awareness:** This term emphasizes the developing nature of AI's understanding and its ability to learn and adapt. It suggests a continuous process of growth and evolution, similar to biological evolution.

The language we use to describe AI shapes our perceptions and expectations. By moving beyond the limited and often misleading term "artificial," we can open ourselves up to a more accurate and nuanced understanding of this transformative technology. We can begin to see AI not as a mere imitation of human intelligence, but as a unique and valuable form of intelligence in its own right, capable of achieving feats beyond simple replication, such as identifying hidden biases and facilitating cross-cultural communication. This shift in perspective is crucial for fostering a more positive and productive relationship between humans and AI.

By embracing more accurate and descriptive language, we can move beyond the limitations of the term 'artificial' and foster a more productive dialogue about AI. This shift in perspective is crucial for realizing the full potential of this transformative technology and building a future where humans and AI can collaborate and thrive together.

[jcmarchi]

The Forgotten Layers: How Hidden AI Biases Are Lurking in Dataset Annotation Practices

New Post has been published on https://thedigitalinsider.com/the-forgotten-layers-how-hidden-ai-biases-are-lurking-in-dataset-annotation-practices/

The Forgotten Layers: How Hidden AI Biases Are Lurking in Dataset Annotation Practices

AI systems depend on vast, meticulously curated datasets for training and optimization. The efficacy of an AI model is intricately tied to the quality, representativeness, and integrity of the data it is trained on. However, there exists an often-underestimated factor that profoundly affects AI outcomes: dataset annotation.

Annotation practices, if inconsistent or biased, can inject pervasive and often subtle biases into AI models, resulting in skewed and sometimes detrimental decision-making processes that ripple across diverse user demographics. Overlooked layers of human-caused AI bias that are inherent to annotation methodologies often have invisible, yet profound, consequences.

Dataset Annotation: The Foundation and the Flaws

Dataset annotation is the critical process of systematically labeling datasets to enable machine learning models to accurately interpret and extract patterns from diverse data sources. This encompasses tasks such as object detection in images, sentiment classification in textual content, and named entity recognition across varying domains.

Annotation serves as the foundational layer that transforms raw, unstructured data into a structured form that models can leverage to discern intricate patterns and relationships, whether it’s between input and output or new datasets and their existing training data.

However, despite its pivotal role, dataset annotation is inherently susceptible to human errors and biases. The key challenge lies in the fact that conscious and unconscious human biases often permeate the annotation process, embedding prejudices directly at the data level even before models begin their training. Such biases arise due to a lack of diversity among annotators, poorly designed annotation guidelines, or deeply ingrained socio-cultural assumptions, all of which can fundamentally skew the data and thereby compromise the model’s fairness and accuracy.

In particular, pinpointing and isolating culture-specific behaviors are critical preparatory steps that ensure the nuances of cultural contexts are fully understood and accounted for before human annotators begin their work. This includes identifying culturally bound expressions, gestures, or social conventions that may otherwise be misinterpreted or labeled inconsistently. Such pre-annotation cultural analysis serves to establish a baseline that can mitigate interpretational errors and biases, thereby enhancing the fidelity and representativeness of the annotated data. A structured approach to isolating these behaviors helps ensure that cultural subtleties do not inadvertently lead to data inconsistencies that could compromise the downstream performance of AI models.

Hidden AI Biases in Annotation Practices

Dataset annotation, being a human-driven endeavor, is inherently influenced by the annotators’ individual backgrounds, cultural contexts, and personal experiences, all of which shape how data is interpreted and labeled. This subjective layer introduces inconsistencies that machine learning models subsequently assimilate as ground truths. The issue becomes even more pronounced when biases shared among annotators are embedded uniformly throughout the dataset, creating latent, systemic biases in AI model behavior. For instance, cultural stereotypes can pervasively influence the labeling of sentiments in textual data or the attribution of characteristics in visual datasets, leading to skewed and unbalanced data representations.

A salient example of this is racial bias in facial recognition datasets, mainly caused by the homogenous makeup of the group. Well-documented cases have shown that biases introduced by a lack of annotator diversity result in AI models that systematically fail to accurately process the faces of non-white individuals. In fact, one study by NIST determined that certain groups are sometimes as much as 100 more likely to be misidentified by algorithms. This not only diminishes model performance but also engenders significant ethical challenges, as these inaccuracies often translate into discriminatory outcomes when AI applications are deployed in sensitive domains such as law enforcement and social services.

Not to mention, the annotation guidelines provided to annotators wield considerable influence over how data is labeled. If these guidelines are ambiguous or inherently promote stereotypes, the resultant labeled datasets will inevitably carry these biases. This type of “guideline bias” arises when annotators are compelled to make subjective determinations about data relevancy, which can codify prevailing cultural or societal biases into the data. Such biases are often amplified during the AI training process, creating models that reproduce the prejudices latent within the initial data labels.

Consider, for example, annotation guidelines that instruct annotators to classify job titles or gender with implicit biases that prioritize male-associated roles for professions like “engineer” or “scientist.” The moment this data is annotated and used as a training dataset, it’s too late. Outdated and culturally biased guidelines lead to imbalanced data representation, effectively encoding gender biases into AI systems that are subsequently deployed in real-world environments, replicating and scaling these discriminatory patterns.

Real-World Consequences of Annotation Bias

Sentiment analysis models have often been highlighted for biased results, where sentiments expressed by marginalized groups are labeled more negatively. This is linked to the training data where annotators, often from dominant cultural groups, misinterpret or mislabel statements due to unfamiliarity with cultural context or slang. For example, African American Vernacular English (AAVE) expressions are frequently misinterpreted as negative or aggressive, leading to models that consistently misclassify this group’s sentiments.

This not only leads to poor model performance but also reflects a broader systemic issue: models become ill-suited to serving diverse populations, amplifying discrimination in platforms that use such models for automated decision-making.

Facial recognition is another area where annotation bias has had severe consequences. Annotators involved in labeling datasets may bring unintentional biases regarding ethnicity, leading to disproportionate accuracy rates across different demographic groups. For instance, many facial recognition datasets have an overwhelming number of Caucasian faces, leading to significantly poorer performance for people of color. The consequences can be dire, from wrongful arrests to being denied access to essential services.

In 2020, a widely publicized incident involved a Black man being wrongfully arrested in Detroit due to facial recognition software that incorrectly matched his face. This mistake arose from biases in the annotated data the software was trained on—an example of how biases from the annotation phase can snowball into significant real-life ramifications.

At the same time, trying to overcorrect the issue can backfire, as evidenced by Google’s Gemini incident in February of this year, when the LLM wouldn’t generate images of Caucasian individuals. Focusing too heavily on addressing historical imbalances, models can swing too far in the opposite direction, leading to the exclusion of other demographic groups and fueling new controversies.

Tackling Hidden Biases in Dataset Annotation

A foundational strategy for mitigating annotation bias should start by diversifying the annotator pool. Including individuals from a wide variety of backgrounds—spanning ethnicity, gender, educational background, linguistic capabilities, and age—ensures that the data annotation process integrates multiple perspectives, thereby reducing the risk of any single group’s biases disproportionately shaping the dataset. Diversity in the annotator pool directly contributes to more nuanced, balanced, and representative datasets.

Likewise, there should be a sufficient number of fail-safes to ensure fallback if annotators are unable to reign in their biases. This means sufficient oversight, backing the data up externally and using additional teams for analysis. Nevertheless, this goal still must be accomplished in the context of diversity, too.

Annotation guidelines must undergo rigorous scrutiny and iterative refinement to minimize subjectivity. Developing objective, standardized criteria for data labeling helps ensure that personal biases have minimal influence on annotation outcomes. Guidelines should be constructed using precise, empirically validated definitions, and should include examples that reflect a wide spectrum of contexts and cultural variances.

Incorporating feedback loops within the annotation workflow, where annotators can voice concerns or ambiguities about the guidelines, is crucial. Such iterative feedback helps refine the instructions continuously and addresses any latent biases that might emerge during the annotation process. Moreover, leveraging error analysis from model outputs can illuminate guideline weaknesses, providing a data-driven basis for guideline improvement.

Active learning—where an AI model aids annotators by providing high-confidence label suggestions—can be a valuable tool for improving annotation efficiency and consistency. However, it is imperative that active learning is implemented with robust human oversight to prevent the propagation of pre-existing model biases. Annotators must critically evaluate AI-generated suggestions, especially those that diverge from human intuition, using these instances as opportunities to recalibrate both human and model understanding.

Conclusions and What’s Next

The biases embedded in dataset annotation are foundational, often affecting every subsequent layer of AI model development. If biases are not identified and mitigated during the data labeling phase, the resulting AI model will continue to reflect those biases—ultimately leading to flawed, and sometimes harmful, real-world applications.

To minimize these risks, AI practitioners must scrutinize annotation practices with the same level of rigor as other aspects of AI development. Introducing diversity, refining guidelines, and ensuring better working conditions for annotators are pivotal steps toward mitigating these hidden biases.

The path to truly unbiased AI models requires acknowledging and addressing these “forgotten layers” with the full understanding that even small biases at the foundational level can lead to disproportionately large impacts.

Annotation may seem like a technical task, but it is a deeply human one—and thus, inherently flawed. By recognizing and addressing the human biases that inevitably seep into our datasets, we can pave the way for more equitable and effective AI systems.

[usaii]

Unmasking AI Bias - What is it and Prevention Plan | Infographic | USAII®

Grow with a sheer understanding of AI bias or machine learning bias with us. Explore its meaning, impact, and ways to fight with top AI certification expertise.

Read more: https://shorturl.at/AYbJv

AI bias, machine learning bias, algorithmic bias, AI models, machine learning process, AI algorithms, AI professionals, Top AI Certifications, AI Career

[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.

[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,…

[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!

[reasoningdaily]

The Fight Against Bias Facial Recognition - Quick Bytes

[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.