Cristina Ferraz’s Post

How do you identify, define, and prioritize the ethical principles your company needs to engage in AI responsibly? One of the first steps in applying ethics to AI is to determine what ethical principles need to be translated into practice. But where do these values come from, how do we ensure they are the right values, and what needs to be done to translate these values into effective AI Ethics policies? Join the EI Network founder and AI Ethicist, Olivia Gambelin, in an interactive session on how to identify the necessary ethical values for your company, define these values to maximize impact, and create comprehensive policies to navigate through ethical tradeoffs. Agenda: 📍 The business case for ethical values in AI development 📍 What is an ethical value, and where do you find them? 📍 Identify your AI Ethics value set: A framework for building your comprehensive list of values 📍 How to define your ethical values to create clarity and action 📍 Developing an AI Ethics policy to handle ethics tradeoffs Learning outcomes: 👉 Ability to identify, assess, and analyze sets of ethical values for your company 👉 Understand how to prioritize different ethical values to reduce friction in application 👉 Be able to effectively lead conversations to develop living definitions for values 👉 Certification upon completion of the workshop #ethicalintelligence #einetwork #aiethics #aipolicy #responsibleai #aigovernance https://lnkd.in/gA_N7xdX

Successful AI Ethics & Governance at Scale: Bridging The Organizational and Implementation Gaps https://ift.tt/q6YBKlb The Road From Ethics to Executives Winds Through Lawyers and LLMs Designed by Freepik When it comes to AI governance, what do the world’s largest AI companies, the world’s smartest AI academics, and the world’s most famous consulting firms have in common? None of them are responsible for actually making it work in your company. This is part 2 of a series on successful implementing AI ethics and governance in large organizations. Part 1 talks about the challenge of interpretation: how specialist talent is needed to bridge the gap between high level policies and unique AI use cases. In this article we talk about the next two gaps: the organizational gap looking at the challenge of AI ethics and governance ownership spread across different departments, and the implementation gap of reluctance to implement scaled AI ethics and governance measures under pressure to adopt AI. The focus is on AI ethics and governance at scale — in a way that is embedded in the core processes and decisions of the company. Starting on AI ethics is easy — the problem is they often end with the 3 Ps of principles, pilots and PR (public relations). Munn (2022)’s provocative paper ‘The Uselessness of AI Ethics’ encapsulates this well: The failure of AI ethical principles is not spectacular but silent, resulting in the desired outcome: business as usual. With that, let’s jump in. The Organizational Gap In AI Ethics and Governance: Multidisciplinary Collaboration is Hard The evolving AI landscape presents unique challenges in aligning AI ethics and governance with existing organizational structures. Spoiler: There isn’t much alignment out of the box — it is a new set of skills which will likely spawn a new function, but today is distributed across multiple teams. Until this is solved, there will be a dissonance between theoretical frameworks of AI ethics and how it plays out practically when it comes to ownership, funding and decision rights. Many teams have a stake in it, but by that same fact few teams grasp its full scope. Every team brings its own lens to AI ethics and governance, and each has a high-resolution view of their own part of the puzzle, and a low-resolution view of other teams which are incomplete at best and assumption-filled caricatures at worst. Such misalignments lead to organizational tension, resulting from differing perspectives and levels of understanding among various stakeholders. Drivers of this gap include: AI Ethics and Governance Capabilities Operating in Organizational Silos Any implementation effort attempting to solve AI ethics and governance needs to overcome the siloed nature of organizations. Different departments such as data science, cybersecurity, and compliance often operate independently, leading to a fragmented approach to AI ethics. These departments also have multiple sub-teams, compounding...

Successful AI Ethics & Governance at Scale: Bridging The Organizational and Implementation Gaps The Road From Ethics to Executives Winds Through Lawyers and LLMs Designed by Freepik When it comes to AI governance, what do the world’s largest AI companies, the world’s smartest AI academics, and the world’s most famous consulting firms have in common? None of them are responsible for actually making it work in your company. This is part 2 of a series on successful implementing AI ethics and governance in large organizations. Part 1 talks about the challenge of interpretation: how specialist talent is needed to bridge the gap between high level policies and unique AI use cases. In this article we talk about the next two gaps: the organizational gap looking at the challenge of AI ethics and governance ownership spread across different departments, and the implementation gap of reluctance to implement scaled AI ethics and governance measures under pressure to adopt AI. The focus is on AI ethics and governance at scale — in a way that is embedded in the core processes and decisions of the company. Starting on AI ethics is easy — the problem is they often end with the 3 Ps of principles, pilots and PR (public relations). Munn (2022)’s provocative paper ‘The Uselessness of AI Ethics’ encapsulates this well: The failure of AI ethical principles is not spectacular but silent, resulting in the desired outcome: business as usual. With that, let’s jump in. The Organizational Gap In AI Ethics and Governance: Multidisciplinary Collaboration is Hard The evolving AI landscape presents unique challenges in aligning AI ethics and governance with existing organizational structures. Spoiler: There isn’t much alignment out of the box — it is a new set of skills which will likely spawn a new function, but today is distributed across multiple teams. Until this is solved, there will be a dissonance between theoretical frameworks of AI ethics and how it plays out practically when it comes to ownership, funding and decision rights. Many teams have a stake in it, but by that same fact few teams grasp its full scope. Every team brings its own lens to AI ethics and governance, and each has a high-resolution view of their own part of the puzzle, and a low-resolution view of other teams which are incomplete at best and assumption-filled caricatures at worst. Such misalignments lead to organizational tension, resulting from differing perspectives and levels of understanding among various stakeholders. Drivers of this gap include: AI Ethics and Governance Capabilities Operating in Organizational Silos Any implementation effort attempting to solve AI ethics and governance needs to overcome the siloed nature of organizations. Different departments such as data science, cybersecurity, and compliance often operate independently, leading to a fragmented approach to AI ethics. These departments also have multiple sub-teams, compounding the issue. For all the...

🌟 Exciting News in the World of AI Ethics! 🌟 Thrilled to share the success of the recent thought-provoking discussion on AI ethics hosted at the University of Geneva. Esteemed speakers like @KaiZenner, @CaroRobson, @GiuseppeUgazio, @IlonaMaklakova, @RuggeroHuesler, and expert moderator Jean-Marc Seigneur led captivating conversations on the critical topic of ethical AI development. The event highlighted key ethical considerations in AI, including algorithmic bias, privacy protection, transparency, and human-centric design. It emphasized the importance of ethical governance and collaboration to ensure AI technologies align with fundamental ethical principles for the betterment of humanity. Kudos to the sponsors who played a pivotal role in making this event a success and fostering dialogue around ethical AI development. Let's continue to advance ethical AI by fostering collaboration, promoting governance frameworks, enhancing public understanding, investing in research, and nurturing ethical AI talent. Excited to see how these discussions will shape the future of AI technology for the greater good of society. Together, let's embark on this vital journey guided by shared values and a deep commitment to the world of AI ethics. 💡✨ Check out the full event recap here: https://lnkd.in/dva6Dxmd #AI #Ethics #UniversityOfGeneva #ThoughtLeadership #AIforGood #GlobalAIAssociation #TechEthics #FutureTech #InnovationEthics

Is AI Ethics Just Virtue Signaling by the Tech Elite? A Nietzschean Perspective 🤔 There's an uncomfortable parallel between the current AI ethics discourse and Nietzsche's critique of conventional morality that we need to discuss. Nietzsche argued that traditional ethics were created by and for the "weak" - a system of constraints that protected those without power while limiting the potential of the Übermensch. Looking at today's AI ethics landscape, aren't we seeing something similar but inverted? The tech giants and AI companies championing ethical AI guidelines are, in fact, the powerful - not the weak. They're setting the rules of engagement while simultaneously pushing the boundaries of what's possible. This raises an interesting question: Is AI ethics becoming a sophisticated form of moral licensing that allows tech companies to appear conscientious while pursuing their ambitions? Consider this: When was the last time you saw a struggling AI startup prioritizing ethics over survival? The luxury of ethical consideration seems reserved for those who have already "made it." The small players are too busy trying to keep up, much less worry about the philosophical implications of their work. The irony wouldn't be lost on Nietzsche: The very companies that have achieved "Übermensch" status in the tech world are now creating moral frameworks that could potentially limit the rise of newcomers. But perhaps there's a deeper truth here: Are we using ethics as a shield rather than a sword? As a way to feel good about our progress while avoiding the harder questions about power, control, and the future of human agency? What do you think? Is AI ethics becoming the ultimate corporate virtue signal, or is there something more substantial at stake? #AIEthics #Philosophy #Technology #Leadership #Innovation #TechPhilosophy

In my recent article, "It’s Past Time to Pay Attention to AI Ethics", I explore the critical importance of embedding ethical principles into #AI development and use. From creating policies that prevent harm and bias to promoting transparency and trust, the article highlights practical steps organizations can take to ensure responsible AI practices. With only 34% of organizations giving sufficient attention to AI ethics (according to a recent ISACA poll), it’s clear there’s significant room for improvement. By prioritizing ethics, we can not only minimize risks but also position AI as a driver of innovation and sustainable growth. Read the full article on CIO Influence: https://lnkd.in/dvfGSc8e   #Ethics #CIOInfluence #ArtificialIntelligence #DigitalTrust #ISACA

In this AI Marketing Ethics Digest issue, we step beyond our focus on AI marketing ethics to address a critical topic — the need for companies to establish an AI ethics council or committee NOW. We discuss why we should do it, who should comprise it, and what duties it performs. #AI #AIethics #AImarketing

Who should be in charge of AI ethics at your organisation? Most clients tell me that no one is responsible "at this stage" or that the responsibility falls to the CTO as they are leading the rollout of AI. It's a mistake to think of AI as simply a technology. The implications of AI touch the customer, employees and the whole value proposition of the organisation. Some organisations plan to adopt a collaborative model where every department and individual has a responsibility for AI ethics following mandatory training. While I applaud efforts to make AI ethics everyone's job, it's not sufficient to ensure AI is used responsibly. This could be an opportunity to invest in training for a small number of employees as AI ethicists. It may not be a full-time job initially but could be something that one of them grows into. Having an individual with an in-depth understanding of AI, a comprehensive grasp of ethical principles and the authority as a go-to person in the organisation for any questions, would be a good starting point. It's also important that the whole C-suite take the issue of AI ethics seriously. When mistakes are made with AI, they can escalate quickly and cause serious financial or reputational damage. Therefore I'd recommend putting in place a comprehensive AI governance framework that clearly states who can make decisions on what aspects of AI. It should include reviews, risk assessments and a cross-functional ethics board to address ethical issues. I encourage my clients to take the time to work through what issues might arise. By engaging customers, employees and industry experts, you will have a much more informed view of what matters. If you are building your own AI model, the stakes are even higher and it is essential that you work from the ground up. You need to build ethical considerations into every step of the process from design thinking, data collection, algorithm development, and deployment strategies. Taking a strong position on responsible and ethical AI from the beginning will not stifle innovation. It will protect the organisation, the individual leaders, customers, employees and stakeholders in the long-term and position the organisation as a leader in responsible AI. Share your thoughts. Who do you believe should champion AI ethics? #AI #responsibleAI #ethics #bias Image prompt: create an image in sketch format of a metaphor for responsible AI illustrating the balance between technology and ethical AI

AI ethics are too often viewed through a polarizing lens, but the real challenge lies in fostering a culture of transformation that balances innovation with responsibility. 💡 The intersection of technology, ethics, and culture—why AI ethics can't be a one-size-fits-all solution 📊 Turning ethics into numbers: actionable metrics for fairness, transparency, and accountability in AI 🌎 How cultural differences shape ethical standards for AI globally 🔄 The fluid nature of AI: ethical standards today may not be relevant tomorrow 💼 The role of domain experts in creating adaptable ethical frameworks across industries 🔍 Unpacking the unintended consequences of narrowly defined ethical metrics Ready to explore how we can shape a transformative culture that drives ethical AI development? Let’s dive into the complexities and start the conversation! #AI #EthicsInAI #DigitalTransformation #AIInnovation #ResponsibleAI #TechEthics #AIRegulation #EthicalAI #CulturalDiversity #FutureOfAI #TrustworthyAI #AIDevelopment #EthicalTech #AIandSociety #AIAccountability https://lnkd.in/ejPFZh2J

AI ethics boards aren’t supposed to stifle innovation. ‘Ethics’ are often considered rigid and a gatekeeper to innovation. But in 2024, ethics boards are the guardian for innovation. They’re more important than ever before. Their aim: figuring out how to best assess + mitigate risks to help a company innovate, responsibly. Often, AI ethics boards are stagnant. They’re set up once and operate rigidly. Structures don’t change and neither do the people on the board. Instead, ethics boards need to expand and contract as required. With innovation sprinting ahead, they need to keep pace to ensure that AI remains trustworthy, responsible and ultimately, ethical. Fresh perspectives need to be added and adjustments to the review process encouraged. This is an interesting article: https://lnkd.in/eP-Mz-Wh