Why Corporate Purpose And AI Ethics Must Be A Leadership And Risk Management Priority (Blog Series 2 Of 5)

AI Why Corporate Purpose And AI Ethics Must Be A Leadership And Risk Management Priority (Blog Series 2 Of 5) Cindy Gordon Contributor Opinions expressed by Forbes Contributors are their own. CEO, Innovation Leader Passionate about Modernizing via AI New! Follow this author to improve your content experience. Got it! Jul 31, 2022, 10:55am EDT | New! Click on the conversation bubble to join the conversation Got it! Share to Facebook Share to Twitter Share to Linkedin Ethics Honest and Integrity: Building Blocks for Corporate Purpose getty Why Corporate Purpose And AI Ethics Must Be a Leadership And Risk Management Priority In my last blog, I introduced the meaning of corporate purpose and positioned the decline of human happiness, and the increasing importance of new operating frameworks linking corporate purpose to ethical AI, and happiness economics.

See Blog One in this five part series here. This blog will provide more context on what the field of AI is doing to advance corporate purpose in light of AI ethical frameworks, principles or guidelines that are shaping our data foundations and continue to learn our greatest asset with long-term value is the quality of data that we produce and the risks we face for not managing and protecting our data assets. As audit practices evolve, auditors will increase their data valuation expertise and examine in detail the control pipes that flow under corporate operating structures to determine both value and risks to a business.

The majority of a company’s internal CFO’s or auditors are not sufficiently trained and certified on data management practices and most external auditors are not highly skilled in data audit and data valuation controls. As evidenced by E&Y, “the regulatory environment needs to evolve so that auditors can make more effective use of data and data analysis techniques. A limiting factor today, from the auditor’s perspective, is not the capability of their technology, but how far existing audit standards will allow them to apply this in place of traditional methods.

” MORE FOR YOU Black Google Product Manager Stopped By Security Because They Didn’t Believe He Was An Employee Vendor Management Is The New Customer Management, And AI Is Transforming The Sector Already What Are The Ethical Boundaries Of Digital Life Forever? While the enlightened auditors, like EY, are influencing the regulatory environments, we need to increase our levels of digital literacy across all organizational functions. New vocabulary is fast emerging and board directors and CEO’s must upgrade their corporate knowledge of data, AI and align to stronger corporate purpose and ethical AI foundations to modernize their operations. Understanding words such as: exabyte (10 18 bytes), zettabyte (10 21 bytes) and yottabyte (10 24 bytes), and the language of AI like: NarrowAI, General AI, AI model, supervised learning, unsupervised learning, deep learning, etc.

Thousands of new words must be mastered to lead in a data value chain world. See my prior blogs on AI BrainTrust to start your learning journey. The majority of companies that we work with are at different stages of data and digital transformation, but few have accountability of all functional leaders to have a data management and literacy plan that is integrated enterprise wide.

Most leaders rely on their Chief Information Officers (CIOs) or CTO’s to ensure the technology layers have data management controls in place but the data lineage logic of what data attributes mean across the enterprise and controls to centralize core knowledge data assets more often looks like the wild west as end users continue to create models in excel, in functional server architectures to work around the formal data management practices put in place, and most CEOs don’t dig deep into inspecting data lineage practices, hence, companies continue to proliferate data in diverse unstructured repositories and the data value chains get messier and messier. Board Directors must increase their leadership knowledge in AI and Data Modernization as the valuation of companies long term will face major value chain risks The majority of companies are still struggling with the common challenge of how to transform their business model by turning data from a cost (acquire, store, protect, manage and distribute) into an economic value engine aligned with corporate purpose. While this data proliferation explosion dynamic is going on, companies are investing in AI and data enabling technologies to process data and get a handle on AI enablements.

Technologies like: voice recognition, natural language processing (sentiment mining), augmented reality, computer imaging, robotics, ML algorithms, cloud computing are all investments striving to create a more intelligent enterprise – but will the corporate purpose and controls be sufficiently in place to realize the value? What companies consistently are not doing is developing an enterprise wide data modernization and integrated AI strategy where all strategic programs, and AI or technology investments are integrated with consistent value realization metrics, and furthermore are integrated into the company’s strategic and operating plans. Underlying all these modernization operating structures, corporate purpose and trust must be the unifying glue, so the new intelligence value chain is transparent and is trusted. This is an enormous planning task to also ensure that all value chains across diverse distribution channels embed consistent operating principles and inspection systems and auditors have a major role to play to ensure B2B, B2C and increasing business models are integrating B2B /B2C – hence additional complexities are increasing and board directors and CEOs need to embed deeper corporate purpose and trust foundations to manage risk and increase value of data assets.

As I wrote in my prior blog in this series: UBS reports that AI as a standalone industry will possibly be worth USD $100 billion by 2030 and USD $500 billion by 2050, the investments made today will likely bear tremendous fruit in the decades ahead. The UBS long-term investment themes of robotics and automation, digital data and e-commerce would directly benefit from such high rates of consistent growth. Conclusion: What the next 30 years will look like will be like the 1930’s and 1940’s as every business model will evolve with more intelligence and board directors and CEO’s must accelerate their digital literacy and ensure that their auditors are certified in data management practices and talking to front line employees to get the real facts.

There are many trains for data to easily travel on, but without controls in data modelling, islands of knowledge are flourishing, with limited operating controls and hence value is decreased to short term thinking vs. long term shared intelligence thinking. It is important to understand that the laws in these areas are still emerging but they are coming and board directors and CEO’s must be ready vs scramble.

The EU General Data Protection Regulation (GDPR) has the world’s toughest data protection laws, with fines up to €20 million or 4% of worldwide revenue — whichever is higher. Since the law took effect in 2018, more than 1500 fines have been issued, and expended to further double in 2023. As AI systems require massive amounts of data for training, without proper governance practices, it’s easy to risk data privacy laws when implementing AI.

The European Union is also working on an AI Act , which will create a new set of regulations specifically around artificial intelligence. The AI Act was first proposed in the spring of 2021 and may be approved as soon as 2023. Failure to comply will result in a range of punishments, including financial penalties up to 6% of global revenue, even higher than the GDPR.

It is imperative all board directors and audit committees, take seriously corporate purpose and ensure data management and AI practices are trusted. To further extend your digital literacy and corporate purpose learning journey in the context of AI Trusted Ethics, here are a few additional sources of value. As a teaser, you can read our company’s AI Ethics policy .

You can also find leadership insights on AI best practices in one of our latest books, The AI Dilemma. Additional knowledge sources: 1. ) A unified framework of AI for Society.

2. ) Ethical AI Frameworks put togeth er by the Council of Europe. 3.

) Australia’s AI Ethical Framework 4. ) Unesco Recommendations on AI Ethics 5. ) Ethical AI Frameworks are not Enough (Harvard) 6.

) Accenture ViewPoint on AI Ethics 7. ) AI Ethics : European Economic Forum 8. ) AI Ethics – Regulatory Status in Canada (Legal ) 9.

) USA Office of the Director of Intelligence – AI Guidelines 10. ) IEEE AI Standards 11. ) EY : Ethics and AI White Paper 12.

) EY Trusted AI Audit Approach My next blogs will focus on Corporate Purpose and AI Ethics and include Board Director and CEO questions to ask of your CIO/CTO and the following blog questions to ask of your Auditors (Internal and External) to support your risk management and fiduciary leadership responsibilities to modernizing your organizations. Follow me on Twitter or LinkedIn . Check out my website or some of my other work here .

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