Ernest Chaney’s Post

Our latest free issue of our AI Marketing Ethics Digest, written by co-editor Brianna Blacet is out. It asks whether organizations need an AI ethics policy specific to marketing. What's your opinion? #AIethics #marketing #AImarketingethics

We are taking a stand! There are so many good reasons why marketing groups should have their own AI Ethics policies. Here are just a few: - marketers collect, store, and use data differently than other groups in a typical company - AI has changed the way we interact with customers - the advent of new technology and software has blurred the rules of engagement There are many more reasons, but you'll have to check out the latest issue of AI Marketing Ethics Digest to find out. (While you're there, don't forget to subscribe. It's free!) If you're a business or marketing leader, don't miss this one. And I'm not saying that just because I wrote it...😂

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

Lawyers using AI must heed ethics rules, ABA says in first formal guidance LINK: https://lnkd.in/eRErxGqh #TheLegalLowdown #InLitigationNews Please LIKE & REPOST

Google Restructures AI Ethics Oversight Amidst Concerns #AI #AIprinciplesreviews #AItechnology #artificialintelligence #CEO #Google #leadershipchange #llm #machinelearning #RESIN #ResponsibleAIdevelopment #restructuring #Techgiants

Successful AI Ethics & Governance at Scale: Bridging The Interpretation Gap https://ift.tt/7el53it Principles that generalize require professionals who specialize Photo by Una Laurencic AI ethics and governance has become a noisy space. At last count, the OECD tracker counts over 1,800 national-level documents on initiatives, policies, frameworks, and strategies as of September, 2024 (and there seems to be consultants and influencers opining on every one). However, as Mittelstadt (2021) succinctly puts in a way that only academic understatement can, principles alone cannot guarantee ethical AI. Despite the abundance of high-level guidance, there remains a notable gap between policy and real-world implementation. But why is this the case, and how should data science and AI leaders think about it? In this series, I aim to advance the maturity of practical AI ethics and governance within organizations by breaking down this gap into three components, and draw from research and real world experience to propose strategies and structures that have worked in implementing AI ethics and governance capabilities at scale. The first gap I cover is the interpretation gap, which arises from the challenge of applying principles expressed in vague language such as ‘human centricity’ and ‘fairness’ to the diverse range of real-world AI applications that are both built and purchased. Yet accounting for the nuances and complexities of each specific application is critical for effective implementation. The Interpretation Gap in AI Ethics and Governance: Bridging Principles and Real World Practice A significant challenge in the realm of AI ethics and governance is the interpretation and application of broadly defined ethical principles to diverse AI applications. AI ethics principles themselves are not the primary issue. The problem is consensus on principles but divergence in practical interpretation. There is a strong level of harmonization and consensus on the principles that responsible and trusted AI systems should have. Jobin et al. (2019) tabulated principles that appear across the global AI governance landscape and found that principles such as transparency, justice & fairness, non-maleficence, responsibility and privacy appear in over half of all published AI governance documents. Similarly a study by Fjeld et al. (2020) the following year uncovered a growing consensus towards thematic trends which she dubbed an emerging “normative core” of AI ethics principles. However, directing AI scientists and engineers to adhere to these principles in isolation without some form of assistance to interpret them is often unhelpful. This is driven by three reasons: 1. The Language of AI Ethics Principles are Ambiguous by Design The first reason is that the foundational principles of AI ethics, such as human centricity and fairness, are often articulated in a manner that is conceptually broad and open to interpretation. This vagueness is needed...

Successful AI Ethics & Governance at Scale: Bridging The Interpretation Gap Principles that generalize require professionals who specialize Photo by Una Laurencic AI ethics and governance has become a noisy space. At last count, the OECD tracker counts over 1,800 national-level documents on initiatives, policies, frameworks, and strategies as of September, 2024 (and there seems to be consultants and influencers opining on every one). However, as Mittelstadt (2021) succinctly puts in a way that only academic understatement can, principles alone cannot guarantee ethical AI. Despite the abundance of high-level guidance, there remains a notable gap between policy and real-world implementation. But why is this the case, and how should data science and AI leaders think about it? In this series, I aim to advance the maturity of practical AI ethics and governance within organizations by breaking down this gap into three components, and draw from research and real world experience to propose strategies and structures that have worked in implementing AI ethics and governance capabilities at scale. The first gap I cover is the interpretation gap, which arises from the challenge of applying principles expressed in vague language such as ‘human centricity’ and ‘fairness’ to the diverse range of real-world AI applications that are both built and purchased. Yet accounting for the nuances and complexities of each specific application is critical for effective implementation. The Interpretation Gap in AI Ethics and Governance: Bridging Principles and Real World Practice A significant challenge in the realm of AI ethics and governance is the interpretation and application of broadly defined ethical principles to diverse AI applications. AI ethics principles themselves are not the primary issue. The problem is consensus on principles but divergence in practical interpretation. There is a strong level of harmonization and consensus on the principles that responsible and trusted AI systems should have. Jobin et al. (2019) tabulated principles that appear across the global AI governance landscape and found that principles such as transparency, justice & fairness, non-maleficence, responsibility and privacy appear in over half of all published AI governance documents. Similarly a study by Fjeld et al. (2020) the following year uncovered a growing consensus towards thematic trends which she dubbed an emerging “normative core” of AI ethics principles. However, directing AI scientists and engineers to adhere to these principles in isolation without some form of assistance to interpret them is often unhelpful. This is driven by three reasons: 1. The Language of AI Ethics Principles are Ambiguous by Design The first reason is that the foundational principles of AI ethics, such as human centricity and fairness, are often articulated in a manner that is conceptually broad and open to interpretation. This vagueness is needed for...

Ethics committees have a rough time. Frequently complained about, frequently seen as bureaucratic, frequently complex. And utterly essential. Good article here that discusses the potential of AI to reduce the administrative burden and speed up the process. But - and it’s a really important but - a reminder of the importance of robust ethics and the imperative that it remains a fundamentally human process.

🔎Exploring the critical juncture of Artificial Intelligence and Information Ethics. In today's digital realm, understanding the ethical dimensions is vital. ⚖️At CreativCollectivX, we embrace AI's transformative tools while upholding the responsibility they entail. Dive into the nuanced realm where ownership, creator dignity, and information integrity converge.  . . . #AI #informationethics #brandbuilding #marketingagency #socialmediamarketing

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...