Bias Reduction Techniques

„Companies spend millions on antibias training each year in hopes of creating more-inclusive—and thereby innovative and effective—workforces. Studies show that well-managed diverse groups perform better and are more committed, have higher collective intelligence, and excel at making decisions and solving problems. But research also shows that bias-prevention programs rarely deliver“, schreiben Joan C. Williams und Sky Mihaylo in der Harvard Business Review. Statt auf ineffiziente Programme fokussieren die Autorinnen auf Möglichkeiten, die einzelne Führungskräfte in der Praxis haben, um Vorurteilen entgegenzuwirken und Diversität zu verwirklichen. Es beginnt für sie damit, zu verstehen, wie sich Voreingenommenheit im Arbeitsalltag auswirkt, wann und wo ihre verschiedenen Formen tagtäglich auftreten. Das Motto: „You can’t be a great manager without becoming a ‚bias interrupter‘.“  Ihre Empfehlungen gliedern Williams und Mihaylo in drei Hauptpunkte. ▶️ Fairness in hiring: 1. Insist on a diverse pool.  2. Establish objective criteria, define “culture fit” (to clarify objective criteria for any open role and to rate all applicants using the same rubric), and demand accountability.  3. Limit referral hiring.  4. Structure interviews with skills-based questions.    ▶️ Managing Day-to-Day:  Day to day, they should ensure that high- and low-value work is assigned evenly and run meetings in a way that guarantees all voices are heard. 1. Set up a rotation for office housework, and don’t ask for volunteers.  2. Mindfully design and assign people to high-value projects.  3. Acknowledge the importance of lower-profile contributions.  4. Respond to double standards, stereotyping, “manterruption,” “bropriating,” and “whipeating (e.g., majority-group members taking or being given credit for ideas that women and people of color originally offered). 5. Ask people to weigh in. 6. Schedule meetings inclusively (they should take place in the office and within working hours). 7. Equalize access proactively (e.g., if bosses meet with employees, this should be driven by business demands or team needs).   ▶️ Developing your team: Your job as a manager is not only to get the best performance out of your team but also to encourage the development of each member. That means giving fair performance reviews, equal access to high-potential assignments, and promotions and pay increases to those who have earned them. 1. Clarify evaluation criteria and focus on performance, not potential.  2. Separate performance from potential and personality from skill sets.  3. Level the playing field with respect to self-promotion (by giving everyone you manage the tools to evaluate their own performance).  4. Explain how training, promotion, and pay decisions will be made, and follow those rules. „Conclusion: Organizational change is crucial, but it doesn’t happen overnight. Fortunately, you can begin with all these recommendations today.“ #genderequality #herCAREER

A common misconception is that AI systems are inherently biased. In reality, AI models reflect the data they're trained on and the methods used by their human creators. Any bias present in AI is a mirror of human biases embedded within data and algorithms. 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐁𝐢𝐚𝐬 𝐄𝐧𝐭𝐞𝐫 𝐀𝐈 𝐒𝐲𝐬𝐭𝐞𝐦𝐬? - Data: The most common source of bias comes from the training data. If datasets are unbalanced or don't represent all groups fairly - often due to historical and societal inequalities - bias can occur. - Algorithmic Bias: The choices developers make during model design can introduce bias, sometimes unintentionally. This includes decisions about which features to include, how to process the data, and what objectives the model should optimize. - Interaction Bias: AI systems that learn from user interactions can pick up and amplify existing biases. e.g., recommendation systems might keep suggesting similar content, reinforcing a user's existing preferences and biases. - Confirmation Bias: Developers might unintentionally favor models that confirm their initial hypotheses, overlooking others that could perform better but challenge their preconceived ideas. 𝐓𝐨 𝐚𝐝𝐝𝐫𝐞𝐬𝐬 𝐭𝐡𝐞𝐬𝐞 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐚𝐭 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐥𝐞𝐯𝐞𝐥, 𝐭𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐭𝐞𝐜𝐡𝐧𝐢𝐪𝐮𝐞𝐬 𝐬𝐮𝐜𝐡 𝐚𝐬: - Fair Representation Learning: Developing models that learn data representations invariant to protected attributes (e.g., race, gender) while retaining predictive power. This often involves adversarial training, penalizing the model if it can predict these attributes. - Causal Modeling: Moving beyond correlation to understand causal relationships in data. By building models that consider causal structures, we can reduce biases arising from spurious correlations. - Algorithmic Fairness Metrics: Implementing and balancing multiple fairness definitions (e.g., demographic parity, equalized odds) to evaluate models. Understanding the trade-offs between these metrics is crucial, as improving one may worsen another. - Robustness to Distribution Shifts: Ensuring models remain fair and accurate when exposed to data distributions different from the training set. Using techniques like domain adaptation and robust optimization. - Ethical AI Frameworks: Integrating ethical considerations into every stage of AI development. Frameworks like AI ethics guidelines and impact assessments help systematically identify and mitigate potential biases. - Model Interpretability: Utilize explainable AI (XAI) techniques to make models' decision processes transparent. Tools like LIME or SHAP can help dissect model predictions and uncover biased reasoning paths. This is a multifaceted issue rooted in human decisions and societal structures. This isn't just a technical challenge but an ethical mandate requiring our dedicated attention and action. What role should regulatory bodies play in overseeing AI fairness? #innovation #technology #future #management #startups

AI in healthcare isn’t as neutral as you think.  AI could harm the very patients it’s meant to help. Without addressing the bias, we will never be able to benefit from the good. Here’s how we can fix it.  1. 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 AI models are only as good as the data they are trained on. Unfortunately, many datasets lack diversity, often overrepresenting patients from certain regions or demographics. Ensuring datasets are inclusive of all populations is key to reducing bias. 2. 𝗥𝗶𝗴𝗼𝗿𝗼𝘂𝘀 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻 AI tools must be tested across diverse populations before deployment. Studies have highlighted how biased algorithms can worsen health disparities at every stage of development. Rigorous validation ensures that these tools perform equitably for all patients. 3. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Healthcare professionals need to understand how AI models make decisions. Lack of transparency can lead to mistrust and misuse. Explainable AI not only builds trust but also helps identify and correct biases in the system. 4. 𝗠𝘂𝗹𝘁𝗶-𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 Bias mitigation requires collaboration between AI developers, clinicians, policy makers, and patient advocates. Diverse perspectives help identify blind spots and create solutions that work for everyone. 5. 𝗢𝗻𝗴𝗼𝗶𝗻𝗴 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Bias doesn’t stop at deployment. Continuous monitoring is needed to ensure AI tools adapt to new data and evolving healthcare needs. For instance, algorithms trained on outdated or incomplete data may maintain errors over time. Only by addressing these areas, can we see the benefits of AI in healthcare, such as reducing errors, aiding diagnoses, and personalizing treatments for all. What steps are your organization taking to ensure fairness in AI healthcare tools?

🔬 How can we make AI less biased to skin color? Medical AI has a critical problem: bias that can impact diagnostic accuracy. Research has shown that AI trained primarily on lighter skin tones can struggle to detect potential melanomas on darker skin – a significant challenge in healthcare technology. Researchers Peter J. Bevan and Amir Atapour-Abarghouei have developed a solution to address this issue. By adding a gradient reversal layer to their machine learning network, they created a model that learns to diagnose skin lesions without being influenced by skin tone. Their approach prevents the model from predicting a patient's skin type while improving its ability to distinguish between melanoma and benign lesions. The result is more consistent diagnostic performance across different skin tones. This research is an important step towards more equitable healthcare technology. By reducing racial bias in medical imaging, we can work towards ensuring more reliable diagnostic care for all patients. https://lnkd.in/e_x5U8_7 #MedicalAI #HealthTech #AIEthics #HealthEquity __________________ Enjoyed this post? Like 👍, comment 💬, or re-post 🔄 to share with others. Click "View my newsletter" under my name ⬆️ to join 1500+ readers.

Dwight Jackson, a Black man, claims that the Shinola Hotel denied him a job interview because of his race. He knows this, he says, because he reapplied for the same job at the same hotel with the same resume ... with one key difference. He changed his name to John Jebrowski. While the hotel didn't offer Jackson an interview, it did offer one to Jebrowski. That, Jackson says in his recently filed lawsuit, is race discrimination. Inherent bias refers to the attitudes or stereotypes that unconsciously affect our understanding, actions, and decisions. These biases can silently influence hiring decisions, leading to discrimination based on characteristics such as race. Name bias is one example of how inherent biases manifest themselves. What can an employer do to prevent these inherent biases from infecting hiring decisions? Here are 7 suggestions: 1. Implement blind hiring practices by removing identifying information from resumes and applications. 2. Develop a structured interview process with standardized questions for all candidates. 3. Use scorecards to evaluate responses consistently. 4. Train hiring managers on recognizing and mitigating inherent biases. 5. Form diverse interview panels to provide multiple perspectives on each candidate. 6. Analyze hiring data and practices to identify and address patterns of bias. 7. Define clear, job-related criteria for evaluating candidates. Eliminating inherent bias is critical to create fair and inclusive hiring practices, which in turn helps create diverse and inclusive workplaces. It also helps eliminate the risk of expensive and nasty discrimination lawsuits.

People often say what they think they should say. I had a great exchange with 👋 Brandon Spencer, who highlighted the challenges of using qualitative user research. He suggested that qual responses are helpful, but you have to read between the lines more than you do when watching what they do. People often say what they think they should be saying and do what they naturally would. I agree. Based on my digital experiences, there are several reasons for this behavior. People start with what they know or feel, filtered by their long-term memory. Social bias ↳ People often say what they think they should be saying because they want to present themselves positively, especially in social or evaluative situations. Jakob's Law ↳ Users spend most of their time on other sites, meaning they speak to your site/app like the sites they already know. Resolving these issues in UX research requires a multi-faceted approach that considers what users say (user wants) and what they do (user needs) while accounting for biases and user expectations. Here’s how we tackle these issues: 1. Combine qualitative and quantitative research We use Helio to pull qualitative insights to understand the "why" behind user behavior but validate these insights with quantitative data (e.g., structured behavioral questions). This helps to balance what users say with what they do. 2. Test baselines with your competitors Compare your design with common patterns with which users are familiar. Knowing this information reduces cognitive load and makes it easier for users to interact naturally with your site on common tasks. 3. Allow anonymity  Allow users to provide feedback anonymously to reduce the pressure to present themselves positively. Helio automatically does this while still creating targeted audiences. We also don’t do video. This can lead to more honest and authentic responses. 4. Neutral questioning We frame questions to reduce the likelihood of leading or socially desirable answers. For example, ask open-ended questions that don’t imply a “right” answer. 5. Natural settings Engage with users in their natural environment and devices to observe their real behavior and reduce the influence of social bias. Helio is a remote platform, so people can respond wherever they want. The last thing we have found is that by asking more in-depth questions and increasing participants, you can gain stronger insights by cross-referencing data. → Deeper: When users give expected or socially desirable answers, ask follow-up questions to explore their true thoughts and behaviors. → Wider: Expand your sample size (we test with 100 participants) and keep testing regularly. We gather 10,000 customer answers each month, which helps create a broader and more reliable data set. Achieving a more accurate and complete understanding of user behavior is possible, leading to better design decisions. #productdesign #productdiscovery #userresearch #uxresearch

How can we fight #bias in #performance reviews? I really wish there was a single solution for that – but a decent way to address that requires a number of concerted efforts that, sustained over time, will yield the expected outcomes. One of the many things an organisation can do is ensure there are #calibration committees overseeing the activity. That, though, doesn't come without potential unintended consequences: new research suggests these meetings can *introduce* bias into the process in several ways – like exacerbating the tendency to rank employees more toward the middle of the scale, failing to differentiate between high, average, and low performers. Such outcome means average and low performers don’t get crucial feedback about how to improve and risks sending outstanding employees a demoralising message that they are just average. And that's not cool. Here are some tips that the authors – Raafiya Ali Khan, Rachel Korn & Joan C. Williams – share with us in the valuable Harvard Business Review article posted in the comments: 1️⃣ Teach participants what bias looks like: basic unconscious bias training can go a long way, especially if it comes with practical examples of how they can show up in this specific touchpoint of the employee lifecycle. 2️⃣ Use a consistent, concise, evidence-based performance rubric, and have participants submit ratings in writing, in advance: establishing such will help you evaluate each employee on the same job-relevant criteria. 3️⃣ Assign people to look for bias during the meeting: at a previous job we would nickname that role the "bias buster" – participants tasked specifically with looking for evidence of bias during the calibration meeting. By explicitly designating that role, participants felt more confident to speak up when they heard something that merited more discussion. Performance reviews should help employees grow, not sideline them – therefore fair evaluations are vital for a company's success. Bias can be found everywhere, but structured and data-informed processes can mitigate it.

You're going to judge a book by its cover. And you're going to judge a person based on their looks. That's pretty privilege. A bias that isn't just unfair, it's costing you top talent. Your brain makes snap judgments about people in 0.1 seconds based on appearance—before they've spoken a single word. This can be a real problem as subconsciously, your brain will have made a judgment on them on this without you even hearing about their qualifications. It's not intentional, it's a biological hardwiring in our brain that is a holdover from hunter-gatherer times. Less initial information (no visuals) actually leads to better hiring decisions. Why? Because you're forced to focus on what actually matters: skills, experience, and motivation. Some ways you can bypass your brain's biases: → Remove photos from CVs → Conduct voice-only interviews for initial screenings Remember: The best person for the job might not look like what you expect them to look like! Bonus challenge for hiring managers: Audit your process. Where can you remove appearance-based bias? #UnconsciousBias #Neuroscience #DiversityAndInclusion

I’ve worked with enough hiring teams to know that many believe they’re evaluating candidates objectively. What I’ve seen repeatedly is that affinity bias often creeps in unnoticed. We tend to gravitate toward candidates who feel familiar—those who share our background, education, or professional experience. It may feel like a gut instinct, but it can unintentionally limit who gets through the door. In my article for The Future of Talent Acquisition and Recruitment, published by the Intelligent Enterprise Leaders Alliance, I talk about how organizations can manage this bias without overhauling their entire hiring strategy. Here are a few things that help: ✅ Use panel interviews to bring in different perspectives ✅ Stick to structured evaluation rubrics ✅ Compare each candidate to the job description, not to each other Bias can’t be eliminated entirely, but it can be interrupted. Small changes like these lead to better hiring outcomes and more inclusive teams. I’m proud to share this piece alongside other contributors in the report. Download the full study for free—link is in the comments. #InclusiveHiring #DEI #TalentAcquisition #BiasInRecruitment