Trilateral Research’s Post

How do you mitigate bias in AI systems? Without proper attention, biased training data can often result in biased machine learning outputs. This is especially dangerous in the context of complex issues like investigating welfare fraud. The UK government recently admitted that the algorithm they are using to recommend candidates for further investigation in relation to potential fraud was biased in relation to: * Age * Disability * Marital status, and * Nationality.   This raises serious concerns as it is precisely these vulnerable populations (older, single, disabled and migrant individuals) who are often most in need.   On the flip side, it seems clear from last week's Guardian article that the DWP has commissioned technical “fairness” analysis and includes strict human oversight mechanisms for final decision-making. These steps go a long way in identifying, addressing and mitigating the risk of reinforcing historical bias. However, they may not be enough.   Organisations implementing machine learning systems that learn from historical data about people need to take a robust, socio-technical approach to bias assessment throughout the AI lifecycle. It should include: 1. Carefully selecting data based on its quality, availability and relevance 2. Cleaning and preparing the data for machine learning 3. Conducting bias audits of the training data to understand how different categories of people are represented 4. Designing the algorithm to account for any over or under representation in the dataset. 5. Testing the performance of the algorithm for different populations and addressing any issues discovered. 6. Providing comprehensive training for users providing human oversight so that they understand the strengths and limitations of the tool and avoid automation bias. 7. Ensure algorithmic explainability so that the AI system functions as a tool that supports professional judgement. 8. Have an ongoing assurance and performance monitoring programme to catch biased outputs early and often, and support the implementation of mitigation measures.   With these parameters in mind, organisations can implement AI tools with confidence, even tools for vulnerable populations or complex social problems. Get in touch with our experts at Trilateral to find out more: https://lnkd.in/egC8KkEr   #AIGovernance #ResponsibleAI #EthicalAI #AIBias #AILiteracy #AIAssurance   https://lnkd.in/e_H6VEGh.

Formal, sector-specific guidance is urgently needed to ensure ethical and trustworthy AI. As I mentioned in the original post's comments, aligning AI ethics with domain-specific professional standards is crucial. Developing these sector-specific guidance and formal standards is imperative. #AIethics #MachineLearning #ResponsibleAI #ProfessionalStandards #TrustworthyAI

Integrating AI into public service is a game-changer, offering enhanced decision-making and personalized service delivery. However, its success hinges on thoughtful deployment—ensuring ethical data use, minimizing bias, and maintaining human oversight. As AI becomes more embedded in government operations, leaders must understand both its power and limitations to effectively navigate the complexities of modern governance. CivicPlus #LocalGovernment #GovernmentTechnology #GovernmentDigitization #ArtificialIntelligence #ResponsibleAI #OperationalExcellence https://lnkd.in/gx9AaW9E

Integrating AI into public service is a game-changer, offering enhanced decision-making and personalized service delivery. However, its success hinges on thoughtful deployment—ensuring ethical data use, minimizing bias, and maintaining human oversight. As AI becomes more embedded in government operations, leaders must understand both its power and limitations to effectively navigate the complexities of modern governance. CivicPlus #LocalGovernment #GovernmentTechnology #GovernmentDigitization #ArtificialIntelligence #ResponsibleAI #OperationalExcellence https://lnkd.in/gdtkhSjE

Effectively integrating AI into your local government operations requires strategic planning and management. Here's how to ensure success: Clearly Define the Problem: Before diving in, ensure AI aligns with your goals. Is it the right tool for the job? Start Small: Pilot programs are essential to assess effectiveness and identify any unintended consequences. Prioritize Ethical Data Use: Establish clear policies and oversight mechanisms for data collection and usage. Partnering with an experienced local government technology provider is crucial for a smooth and responsible AI integration. Let CivicPlus be that partner. In collaboration with Frase, we offer our clients an AI-powered chatbot solution designed to enhance service delivery: https://lnkd.in/dFDvH4gV #LocalGovernment #EmergingTechnologies #ArtificialIntelligence #ResponsibleAI Route Fifty https://lnkd.in/dB-N5fQp

A very gentle reminder to my attorney friends and Legal Tech friends who are tempted to define AI bias or "fairness" with simple "dictionary definitions". ✅ It's way more complex than that - be careful how you describe these terms to colleagues or clients. These terms are like a giant onion with dozens of layers to peel. 😊 ➡ Please keep these two items in mind. 👇 1️⃣ Algorithmic "transparency" cannot eliminate all bias. 👉 There are limits in using algorithmic transparency in eliminating bias. Studies help demonstrate the difficulty of evaluating algorithms for preventing discrimination, and the need for a socio-technical lens on the challenge. 👉It is insufficient to look for bias in the algorithm alone. 👉 The FTC launched a new unit focused on algorithmic transparency. BUT, that algorithmic transparency would NOT help regulators to foresee uneven outcomes. 👉 While transparency into AI system mechanisms is rarely a direct bias mitigant, transparency enables many critical AI governance functions. Transparency is very important, but should NOT be mistaken for fairness. 2️⃣ Fairness can't be reduced to a concise definition 👉Much of the work in determining fairness criteria involves supervised learning, but the labeled data required for these tasks may not be readily available. This is particularly true for large language models, where the sheer scale of the datasets used for training is prohibitive for proper data labeling. 👉Fairness is dynamic, social in nature, application and context specific, and NOT just an abstract or universal statistical problem. We must adopt a socio-technical approach to fairness in order to have realistic "fairness definitions" for different contexts and task-specific datasets for machine learning model development and evaluation.

Machine learning (ML) algorithms are revolutionising industries by automating decisions and providing insights from vast data. However, the potential for bias within these algorithms poses significant ethical and practical challenges. Ensuring fairness in ML is crucial for fostering trust and achieving equitable outcomes. Understanding Bias: Bias in ML algorithms can originate from various sources, such as biassed training data, improper feature selection, or flawed model assumptions. If the data reflects historical prejudices, the algorithm will likely perpetuate these biases, leading to unfair or discriminatory outcomes. 🔓 Strategies to Mitigate Bias: - Diverse Data Collection: Ensuring that training data represents diverse populations is fundamental. This approach helps mitigate biases that stem from homogeneous datasets. - Bias Detection Tools: Utilising specialised tools to detect and measure bias in ML models can identify potential issues early in the development process. Fairness Metrics: Implementing fairness metrics, such as equalised odds or demographic parity, allows for the evaluation and adjustment of algorithms to promote fair outcomes. - Transparency and Accountability: Developing transparent algorithms and documenting decision-making processes enhances accountability. Stakeholders can understand how decisions are made and ensure they align with fairness principles. - Ongoing Monitoring: Bias mitigation is an ongoing process. Regularly monitoring algorithms and updating them in response to new data or shifting societal norms is essential for sustained fairness. Addressing bias and ensuring fairness in machine learning requires a multifaceted approach involving diverse data, robust detection tools, and continuous monitoring. By prioritising these strategies, we can harness the power of ML while promoting equity and trust in automated decisions. For more insights on fostering fairness in ML, visit us ➡ bit.ly/4aHJXPz #MachineLearning #AI #BiasMitigation #FairnessInAI #DataScience

𝗪𝗲𝗲𝗸 𝟭𝟬: 𝗗𝗮𝘆 𝟱: 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗚𝗣𝗧 𝗠𝗼𝗱𝗲𝗹𝘀 As we harness the power of Generative Pre-trained Transformers for various applications, it's crucial to address the ethical considerations and ensure the deployment of responsible AI practices. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘄𝗶𝘁𝗵 𝗚𝗣𝗧 𝗠𝗼𝗱𝗲𝗹𝘀 Bias and Fairness: GPT models, like any AI system trained on large datasets, can inadvertently learn and perpetuate biases present in their training data. This can lead to outputs that reinforce stereotypes or discriminate against certain groups. Transparency and Explainability: The complexity of GPT models often makes it challenging to understand how decisions are made, which is crucial for applications in areas like healthcare, law, and public services where explainability is mandatory. Misuse Potential: The ability of GPT models to generate realistic text and content can be misused for creating misleading information, impersonation, and propaganda. Ensuring that these models are not used to harm individuals or society is a significant concern. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 𝗳𝗼𝗿 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜 Bias Mitigation: Implementing techniques to detect and reduce bias in training data and model outputs. Regular audits and updates can help in identifying biases and mitigating them over time. Enhancing Explainability: Developing tools and methods that increase the transparency of AI decision-making processes. Efforts to simplify the models’ reasoning and make it understandable to non-experts are essential for building trust and accountability. Use Case Guidelines: Establishing clear guidelines and ethical frameworks for the deployment of GPT models. These guidelines should dictate where and how these models can be appropriately used, emphasizing safety and ethical considerations. Community Engagement and Governance: Involving a broader range of stakeholders in discussions around the use of GPT technology. This includes policymakers, ethicists, end-users, and communities potentially impacted by AI technology. The development and deployment of GPT models carry substantial ethical responsibilities. By prioritizing ethical considerations and responsible AI practices, developers and users can ensure these powerful tools are used for the benefit of society, reinforcing trust and fostering innovation in a controlled and beneficial manner. 𝗡𝗼𝘁𝗲𝘄𝗼𝗿𝘁𝗵𝘆 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://lnkd.in/gqGqz8dK #DeepLearning #MachineLearning #AI #NeuralNetworks #DataScience #TechLearning #Innovation

The AI Act makes it a requirement for leaders of businesses that use AI to have a high-level understanding of how AI works in relation to their organizations. At its core, AI is about applying mathematics and statistics, in the form of machine learning, to large amounts of data to automate or optimize decisions and outcomes. C-suite executives outsourcing their understanding of the role of algorithms is essentially outsourcing their jobs. Read more about my perspectives on the implications of the AI Act in this Tech Times article 👇 *** I am an AI and business strategist, professional keynote speaker, and board director. I am also a K-1 fighter, metal fan, and eternal rebel. Everything I publish is written by my human self, with a touch of AI-enabled research and illustrations. Follow me or connect for thought-provoking and sometimes humoristic perspectives on artificial intelligence and human stupidity.   #artificialintelligence #generativeAI #responsibleai #ethicalAI #sustainableAI #AIAct Bob Strange Marijana Sarolic Robic Jeannette Gorzala Camilla Gjersem Christian Dalsvaag   https://lnkd.in/dnyGsWQ5