Mona Zoet, LLM, CAMS’ Post

Establishing Performance Measures and Targets for AI Fairness The dynamic world of AI is redefining traditional notions of fairness and creating new challenges. As we venture deeper into the AI sphere, we must reconcile these issues with our aim of maintaining integrity and ethical transparency. The first challenge lies in quantifying AI fairness. With various interpretations of what constitutes as fair and impartial, how do we approach setting firm benchmarks and measurable targets? Solution? Our Control Audits SAAS Platform now includes a unique AI Assurance module designed to precisely address these questions. By understanding the nuanced complexities of fairness within the AI realm, this module helps standardise the measurement of AI fairness, setting clear performance measures and targets. The second issue is the rapid evolution of AI techniques. Keeping a consistent track of this evolution, while ensuring the AI fairness doesn't fall by the wayside, is undoubtedly quite an uphill task. Our module, however, turns this challenge on its head. It constantly adapts to the changing AI landscape while ensuring fairness is always at the forefront in AI development. Lastly, we face the predicament of addressing fairness across a diverse range of AI applications. How can we ensure that these applications are not perpetuating bias and unfairness? Again, the AI Assurance module comes to the rescue! Its inspection capability checks each AI application against a predefined fairness benchmark, diagnosing issues and recommending appropriate corrective measures. The true essence of AI lies in its unbiased decision-making abilities. However, achieving this requires a clear understanding of fairness in AI and establishing accurate performance measures. The Control Audits platform, with its new AI Assurance module, promises to navigate this path with you, keeping you one step ahead in your pursuit of AI fairness. To witness this revolution in AI fairness, visit https://controlaudits.com #AI #ArtificialIntelligence #Governance #Risk #Compliance #Assurance #Assessment #Audit

Demystifying the Machine: "Unveiling Transparency and Accountability in AI" Artificial intelligence (AI) is on a meteoric rise, revolutionizing industries and infiltrating every aspect of our lives. However, with this power comes a critical responsibility: ensuring the "ethical" development and use of AI. Lifting the Lid on the "Black Box" Imagine an AI-powered recruitment tool that screens resumes. Without transparency, qualified candidates might be unfairly rejected, with the system offering no explanation for its decision. This lack of "explainability" creates a black box, shrouded in secrecy and fueling concerns about bias. Here's where transparency comes in. By making AI's decision-making processes more understandable, we can build trust and ensure fairness. Ensuring "Accountability" in the Age of AI Accountability is equally important. If an AI used for loan approvals makes discriminatory decisions, who is responsible? Clear lines of accountability are essential. We need frameworks that ensure humans remain ultimately responsible for the actions of AI systems they create and deploy. Building a Robust Framework for Ethical AI Fortunately, AI governance frameworks are emerging to address these concerns. These frameworks establish best practices for the entire AI lifecycle, from development to deployment. Here are some key aspects: Transparency by Design: Embedding explainability mechanisms into AI models from the ground up, allowing us to understand their reasoning. Algorithmic Bias Detection and Mitigation: Proactive identification and mitigation of potential biases in training data and algorithms to prevent discriminatory outcomes. Human-in-the-Loop Systems: Ensuring human oversight and control to guide AI decision-making and intervene when necessary. #EthicalAI isn't just a slogan; it's a necessity. By fostering open dialogue and implementing robust AI governance frameworks, we can harness the immense potential of AI while building trust for a responsible and transparent future. #GoogleAI #ResponsibleAI

The role of artificial intelligence (AI) in claims adjusting seems to create a bit of a debate. Some are super pumped about AI, seeing it as our industry’s next big leap, promising super-efficient processes with reliability and precision. Yet, some are waving the caution flag, worried about what we might lose, especially with respect to the human touch needed in this business. Team “Pro-AI” says: 1. Speed: AI doesn’t sleep, doesn’t take breaks, and moves at a pace that can't be beat by us mere mortals. Proponents declare that this will mean faster claims handling and happier customers. 2. Mistake reducer: Got a huge pile of data? No problem. AI crunches numbers without breaking a sweat, and errors should be a thing of the past.. 3. Cost Cutter: By taking over the grunt work, AI can cut operational costs, freeing up some cash. Team “Caution” counters with: 1. Missing the Human Factor: You just can’t replace the deep understanding that comes from real people talking to real people. Dealing with claims is as much about empathy as it is about evidence. 2. Skills Deterioration: There’s a worry that too much AI might make us rusty in our investigative skills—what happens when the tech glitches or when an oddball case comes up that AI can’t deal with? 3. Ethical Issues: We all know already that there are important ethical questions around privacy and bias in AI systems that could tilt the fairness scale in the wrong direction. What do you think? Are we at a crucial crossroads here? Are you "all in" on AI transforming the claims process, or are you worried it might strip down our roles to just pushing buttons? #ClaimsAdjusting #ArtificialIntelligence #InsuranceIndustry #DigitalTransformation

Artificial Intelligence (AI) - Principles Principles for the ethical use of artificial intelligence have been established to ensure that AI is employed in the best interest of people and the planet, benefiting humanity as a whole. 1. Do no harm: AI systems must not cause or exacerbate harm to individuals or communities, respecting and upholding human rights and freedoms throughout their life cycle. 2. Defined purpose, necessity, and proportionality: The use of AI should be justified and proportionate, aligned with organizational rules and goals. 3. Safety and security: AI systems should be designed, developed, and maintained with robust safety and security measures to prevent harm to humans, the environment, or ecosystems. 4. Fairness and non-discrimination: AI systems should adhere to laws and regulations, avoiding bias, discrimination, and unjustifiable restrictions on human rights. 5. Sustainability: AI should promote sustainability across various domains, with continuous assessment and mitigation of any adverse impacts on society and the environment. 6. Right to privacy, data protection, and data governance: AI systems should prioritize the privacy and rights of individuals, ensuring the integrity and protection of personal data throughout their life cycle. 7. Human autonomy and oversight: AI systems should respect human freedom and autonomy, incorporating human-centric design and ensuring human oversight in decision-making processes. 8. Transparency and explainability: Organizations should use transparent and explainable AI systems, enabling individuals to understand the reasoning behind AI-based decisions that affect them. 9. Responsibility and accountability: Organizations must establish mechanisms for oversight, assessment, and accountability for AI system impacts, including investigating and addressing any harm caused. 10. Inclusion and participation: Organizations should adopt an inclusive and participatory approach in designing, deploying, and using AI systems, engaging with stakeholders and affected communities to identify and mitigate risks and harms. #techvalens #teamtech #AI #principles #softwaredevelopment #AIsustainability #responsibleinnovation #AIequality #AIprinciplesguide

Your invitation to join an exclusive LinkedIn community | AI Regulation in Financial Services   Do you want to stay ahead of the AI regulatory curve? Whether you're a financial services regulatory expert, an operational risk manager, a chief AI officer or simply intrigued by how AI will be contained and regulated in financial services, this closed group is for you. A leading provider of specialist financial services training and consultancy, we are bringing our wealth of AI and regulatory knowledge to the only forum dedicated to exploring the intersection of artificial intelligence and regulatory landscapes within the sector.   As the financial world embraces AI-driven innovations, with the myriad opportunities and challenges it creates, staying ahead of the global regulatory curve is crucial to success across the breadth of financial services. Join us to: 🔍 Gain insights into the latest developments and trends in AI regulation specific to the financial sector. 🤝 Connect with industry experts, regulatory professionals, and like-minded individuals who share your passion. 📚 Access exclusive resources, articles, and discussions curated by our team of experts at ZISHI. 💡 Participate in engaging conversations, share your experiences, and learn from others in the field. Be part of the conversion shaping the future of AI regulation in financial services. Click here for more and to request to join: https://lnkd.in/eihCChsd ZISHI | Resilience Through Knowledge www.thezishi.com | LI: https://lnkd.in/eJ5KcJaR

🌐 The Importance of AI Governance: Ensuring Ethical, Safe, and Fair AI 🌐 Artificial intelligence (AI) is revolutionizing our world, touching every aspect of our lives—from healthcare and finance to law enforcement and beyond. As AI continues to evolve, the importance of AI governance becomes increasingly critical. Why is AI governance essential? 🔍 Upholding Ethical Standards: AI systems make decisions that impact individuals and society. Proper governance ensures these decisions respect human rights and values, promoting transparency, accountability, and inclusivity. 🛡️ Mitigating Risks: Robust safeguards are necessary to prevent privacy violations, security breaches, and unintended consequences. Effective governance frameworks help identify and minimize these risks, protecting users and society. ⚖️ Promoting Fairness: Without careful management, AI can exacerbate social inequalities. Governance policies ensure AI development and deployment are fair and equitable, benefiting all segments of society. 🚀 Fostering Innovation: Clear guidelines and standards prevent substandard AI products, encouraging investment in high-quality AI solutions. Collaborative governance frameworks facilitate shared advancements in AI. 🤝 Building Public Trust: Transparent governance and public involvement are crucial for gaining public trust. When people understand how AI works and feel protected, they are more likely to embrace AI technologies. AI governance is the cornerstone of a future where AI serves humanity positively and equitably. Let's work together to establish and enforce robust governance frameworks, ensuring AI benefits everyone. #AI #Governance #Ethics #Innovation #Trust #Technology #Future --- Feel free to share your thoughts and experiences in the comments!

🚀 How to make AI systems transparent, understandable, and verifiable by humans? That’s the challenge that one of the largest AI companies in the world gave us! Well, challenge accepted! 🌟 #AItransparency is about making AI systems visible, clear, and understandable by humans. It fosters trust, ensures accountability, and enables informed decision-making. Key aspects include: ➡ Data Transparency: Disclosure of data sources and processing. ➡ Algorithm Transparency: Insights into the AI’s logic and parameters. ➡ Model Interpretability: Creating human-understandable models. ➡ Operational Transparency: Information on AI deployment and limitations. ➡ Accountability and Governance: Clear frameworks and responsible parties. ➡ Ethical Considerations: Fair, private, and non-discriminatory AI practices. Right, but how do we make these very complex systems accessible? Human-centricity. ✅ Acknowledging human cognitive biases triggered by so much change in so little time and information overload ✅ User-centric structure of the information and #plainlanguage ✅ Problem-solving design to empower all humans, users, regulators, policymakers to make their own, informed judgment. ❓Why does it matter? #AI is moving fast, and so is the #AIAct It will be published in the Official Journal and will enter into force 20 days after being published, likely in June. ⏰ 6 months after entry into force: “Unacceptable risk AI systems” will be banned—likely December 2024. ⏰ 1 year after entry into force: “General purpose AI models” must comply—likely June 2025. ⏰ 2 years after entry into force: “Limited risk,” “Minimal risk,” and “High risk” AI systems must comply—likely June 2026. 👥 The AI Act can be overwhelming, so we’ve broken down this complex regulation into a series of steps to determine if it applies to your activities. For more info, check out this "AI Act Guide" post: https://lnkd.in/eh7ZRMJM 📘 Transparency is essential for building public trust, ensuring regulatory compliance, and promoting ethical AI use. 🛡️✨ 💫 Regain your freewill online #AIAct #AIRegulation #TechEthics #DigitalTrust #ResponsibleAI

From Green Washing to AI Washing!!! With the rise of Artificial Intelligence, businesses are embracing its potential to revolutionize operations and drive innovation. However, amidst this wave of AI integration, a concerning trend has emerged - AI Washing. Thanks to The Hustle's newsletter for explaining this. AI washing is when companies create false buzz and manipulate investors by: • misleading about the capabilities, limitations, or risks of their AI products • lying about how and when they are using AI This deceptive tactic has consequences too. Regulatory bodies like the US Securities and Exchange Commission (SEC) has charged companies like Delphia (claiming to be a predictor of upcoming companies and trends) and Global Predictions (claiming to be the “first regulated AI financial advisor” and that it offered “AI-driven forecasts") for false claims. This cautionary tale underscores the need for transparency and integrity in AI adoption. You may want to read more about it here: https://lnkd.in/gwvwtgTP Do share your thoughts and comments! #FantasticFriday #industryreadiness #readyforbeyondcovid19world  #trusttheprocess #CampusToCorporate #YouVersion2Point0 #100daysTransformationChallenge #AI #marketing

🚀 How to make AI systems transparent, understandable, and verifiable by humans? That’s the challenge that one of the largest AI companies in the world gave us! Well, challenge accepted! 🌟 #AItransparency is about making AI systems visible, clear, and understandable by humans. It fosters trust, ensures accountability, and enables informed decision-making. Key aspects include: ➡ Data Transparency: Disclosure of data sources and processing. ➡ Algorithm Transparency: Insights into the AI’s logic and parameters. ➡ Model Interpretability: Creating human-understandable models. ➡ Operational Transparency: Information on AI deployment and limitations. ➡ Accountability and Governance: Clear frameworks and responsible parties. ➡ Ethical Considerations: Fair, private, and non-discriminatory AI practices. Right, but how do we make these very complex systems accessible? Human-centricity. ✅ Acknowledging human cognitive biases triggered by so much change in so little time and information overload ✅ User-centric structure of the information and #plainlanguage ✅ Problem-solving design to empower all humans, users, regulators, policymakers to make their own, informed judgment. ❓Why does it matter? #AI is moving fast, and so is the#AIAct It will be published in the Official Journal and will enter into force 20 days after being published, likely in June. ⏰ 6 months after entry into force: “Unacceptable risk AI systems” will be banned—likely December 2024. ⏰ 1 year after entry into force: “General purpose AI models” must comply—likely June 2025. ⏰ 2 years after entry into force: “Limited risk,” “Minimal risk,” and “High risk” AI systems must comply—likely June 2026. 👥 The AI Act can be overwhelming, so we’ve broken down this complex regulation into a series of steps to determine if it applies to your activities. For more info, check out this "AI Act Guide" post: https://lnkd.in/eh7ZRMJM 📘 Transparency is essential for building public trust, ensuring regulatory compliance, and promoting ethical AI use. 🛡️✨ 💫 Regain your freewill online #AIAct #AIRegulation #TechEthics #DigitalTrust #ResponsibleAI

🌟 This month, we're diving into a series on AI transparency—a topic that's gaining significant attention and importance. This comes especially in light with the implementation of the AI Act in Europe. If you want to know more about it, here’s our take and insights on the topic: https://lnkd.in/gFw4Cpmq 📚 Last week, we highlighted key aspects of AI and minors. Are AI tools safe for minors? https://lnkd.in/eFVJ5cXr This week, we're diving into a very central topic when it comes to AI: training methods & data privacy 👀 Starting with #AITrainingMethods… You might be wondering, does AI tools connect your ID with your conversations when training their model? Or are the personal identifiers segregated from the conversations? It’s an important question. Essentially, are personal identifiers removed when training these models? And, is the module training based on our conversations on by default, or off? Moreover, is the AI trained to clearly state that it’s not human but a machine learning model? This is a big deal because most people are more inclined to trust what they believe is a human than a machine, and also because anthropomorphism could be a dark pattern. 💡 It’s also very important for all of us to understand the testing methods applied to the models. For example, Red Teaming: internal and external “ethical hackers” who try to bypass safety and privacy by design measures, to identify any weaknesses and continuously correct them. Think of it as a watchdog ensuring AI is safe and fair, in combination with other testing methods. 🛡️ A lot is happening in Brussels right now about the AI Act. Regulators are debating how detailed AI companies should be about the content used to train their models. Some sections of the law are pretty controversial, especially the part requiring "detailed summaries" of training data. 🤔 ❓ Should this training data be a trade secret, or should AI companies be transparent? What do you think? The EU requests more transparency from AI companies about the data used to train their systems. This is a huge step because it reveals one of the industry's most closely guarded secrets. Since the launch of ChatGPT by OpenAI, there’s been a surge in AI use. But as this tech boom continues, there are rising concerns about how data, especially copyrighted content, is used in training AI models. What’s your take on AI transparency? Should companies reveal more about their training data? Let’s discuss! 🗣️ For more insights, check out the full article: https://lnkd.in/eyxJGr94 💫 Regain your freedom online. #AIAct #AIRegulation #TechEthics #DigitalTrust #ResponsibleAI