AI and the Ethical Conundrum: How organizations can build ethically robust AI systems and gain trust

Capgemini Research Institute

In the wake of the COVID-19 crisis, our reliance on AI has skyrocketed. Today more than ever before, we look to AI to help us limit physical interactions, predict the next wave of the pandemic, disinfect our healthcare facilities and even deliver our food. But can we trust it?

In the latest report from the Capgemini Research Institute – AI and the Ethical Conundrum: How organizations can build ethically robust AI systems and gain trust – we surveyed over 800 organizations and 2,900 consumers to get a picture of the state of ethics in AI today. We wanted to understand what organizations can do to move to AI systems that are ethical by design, how they can benefit from doing so, and the consequences if they don’t. We found that while customers are becoming more trusting of AI-enabled interactions, organizations’ progress in ethical dimensions is underwhelming. And this is dangerous because once violated, trust can be difficult to rebuild.

Ethically sound AI requires a strong foundation of leadership, governance, and internal practices around audits, training, and operationalization of ethics. Building on this foundation, organizations have to:

1. Clearly outline the intended purpose of AI systems and assess their overall potential impact
2. Proactively deploy AI to achieve sustainability goals
3. Embed diversity and inclusion principles proactively throughout the lifecycle of AI systems for advancing fairness
4. Enhance transparency with the help of technology tools, humanize the AI experience and ensure human oversight of AI systems
5. Ensure technological robustness of AI systems
6. Empower customers with privacy controls to put them in charge of AI interactions.

For more information on ethics in AI, download the report.

Ethics In AI: Why Values For Data Matter

Marc Teerlink
forbes.com
Originally posted 18 Dec 19

Here is an excerpt:

Data Is an Asset, and It Must Have Values

Already, 22% of U.S. companies have attributed part of their profits to AI and advanced cases of (AI infused) predictive analytics.

According to a recent study SAP conducted in conjunction with the Economist’s Intelligent Unit, organizations doing the most with machine learning have experienced 43% more growth on average versus those who aren’t using AI and ML at all — or not using AI well.

One of their secrets: They treat data as an asset. The same way organizations treat inventory, fleet, and manufacturing assets.

They start with clear data governance with executive ownership and accountability (for a concrete example of how this looks, here are some principles and governance models that we at SAP apply in our daily work).

So, do treat data as an asset, because, no matter how powerful the algorithm, poor training data will limit the effectiveness of Artificial Intelligence and Predictive Analytics.

The info is here.

The Tricky Ethics of Google's Project Nightingale Effort

Cason Schmit
nextgov.com
Originally posted 3 Dec 19

The nation’s second-largest health system, Ascension, has agreed to allow the software behemoth Google access to tens of millions of patient records. The partnership, called Project Nightingale, aims to improve how information is used for patient care. Specifically, Ascension and Google are trying to build tools, including artificial intelligence and machine learning, “to make health records more useful, more accessible and more searchable” for doctors.

Ascension did not announce the partnership: The Wall Street Journal first reported it.

Patients and doctors have raised privacy concerns about the plan. Lack of notice to doctors and consent from patients are the primary concerns.

As a public health lawyer, I study the legal and ethical basis for using data to promote public health. Information can be used to identify health threats, understand how diseases spread and decide how to spend resources. But it’s more complicated than that.

The law deals with what can be done with data; this piece focuses on ethics, which asks what should be done.

Beyond Hippocrates

Big-data projects like this one should always be ethically scrutinized. However, data ethics debates are often narrowly focused on consent issues.

In fact, ethical determinations require balancing different, and sometimes competing, ethical principles. Sometimes it might be ethical to collect and use highly sensitive information without getting an individual’s consent.

The info is here.

AI Ethics Guidelines Every CIO Should Read

John McClurg
www.informationweek.com
Originally posted August 7, 2019

Here is an excerpt:

Because AI technology and use cases are changing so rapidly, chief information officers and other executives are going to find it difficult to keep ahead of these ethical concerns without a roadmap. To guide both deep thinking and rapid decision-making about emerging AI technologies, organizations should consider developing an internal AI ethics framework.

The framework won’t be able to account for all the situations an enterprise will encounter on its journey to increased AI adoption. But it can lay the groundwork for future executive discussions. With a framework in hand, they can confidently chart a sensible path forward that aligns with the company’s culture, risk tolerance, and business objectives.

The good news is that CIOs and executives don’t need to come up with an AI ethics framework out of thin air. Many smart thinkers in the AI world have been mulling over ethics issues for some time and have published several foundational guidelines that an organization can use to draft a framework that makes sense for their business. Here are five of the best resources to get technology and ethics leaders started.

The info is here.

AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations

Floridi, L., Cowls, J., Beltrametti, M. et al.
Minds & Machines (2018).
https://doi.org/10.1007/s11023-018-9482-5

Abstract

This article reports the findings of AI4People, an Atomium—EISMD initiative designed to lay the foundations for a “Good AI Society”. We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations—to assess, to develop, to incentivise, and to support good AI—which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society.