Editor

As nations across the world slowly reopen their economies after extended lockdowns, businesses will need to hit the ground running to operate in a new abnormal. One of the ways companies can count on meeting the acceleration with safety is by adopting smart tech, especially tools and platforms enabled by artificial intelligence.

However, because these tools and platforms are built on algorithms, there is concern that the use of AI technology might unconsciously result in and perpetuate biases. When it comes to this area, a business’s commitment to ethical operation is a must in a more transparent world where consumers are keenly aware of a company’s track record and business conduct.

What can businesses do to effectively tackle this challenge? How can organizations safely deploy platforms enabled with AI to do more with less while ensuring that they are always doing the right thing?

Enterprises can undertake five best practices to ensure the adoption of AI does not go against the established rules of ethical corporate behavior.

First, organizations must have a clear understanding of what practicing ethical AI means to them and communicate this clearly to stakeholders. These communications should convey the core values that define a business, whether it is transparency, customer delight, or people-focus. An ethical application of AI will then mean that none of these values are compromised or watered down irrespective of the corporate function that executes it.

Second, businesses must invest in ensuring a more ethical application of AI by employing a chief AI ethics officer. This clearly defined position would be in charge of overseeing, limiting, and assessing how AI is embedded into an enterprise system. This would help to restrain a company from allowing AI to be used to carry out inappropriate or controversial functions, such as facial recognition.

Third, companies need to incorporate an AI ethics and quality assurance review as an integral part of the product development and release life cycles, including a focus on various use case scenarios and resulting outcomes. Every new AI-enabled product should be examined from an ethical lens to confirm that it adheres to established protocols around data safety and compliance.

Fourth, enterprises can ensure ethical AI deployment by turning to the customer. A cross-section of experts selected from a company’s advisory council can be leveraged in the testing of newly created AI enabled tools. Their inputs and experience can be funneled back into the product cycle to help the solution become more transparent, fair and impartial, safe, and ethical.

Finally, the fifth way for a business to adopt AI while staying within the confines of regulatory compliance and ethics is to remain transparent about how data is used to build algorithms. Since these algorithms tend to be opaque and complex, an enterprise, while balancing IP interests, may consider going beyond the call of duty to explain and describe to its customers what data is being sourced, and for what purpose.

Making a clearer link between the value data offers to build efficient algorithms and its potential ability to deliver superior customer experience will go a long way in assuaging customer concerns about the ethical deployment of AI to deliver products and services.

The original article was published here.

To support for AI technology and development for social impact, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. Regarding to AI Ethics, AI World Society (AIWS.net) initiated and promoted to design AIWS Ethics framework within four components including transparency, regulation, promotion and implementation for constructive use of AI. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

When​ I first studied artificial intelligence in the 1980s, my lecturers assumed that the most important property of intelligence was the ability to reason, and that to program a computer to perform intelligently you would have to enable it to apply logic to large bodies of facts. Logic is used to make inferences. If you have a general rule, such as ‘All men are mortal,’ and a specific fact, ‘Socrates is a man,’ you, or your computer, can deduce that Socrates is mortal. But it turns out that many of the problems we want intelligent computers to help us with can’t straightforwardly be solved with logic. Some of them – the ability to recognise faces, for example – don’t involve this kind of reasoning. In other cases – the diagnosis of disease would be an example from my own field – the difficulty lies in how to describe the concepts that the rules and facts express. The problem is often seen as a matter of how to standardise terminology. If you want a doctor’s computer to use rules to infer what is wrong with a patient, these rules must be expressed using the same words as the ones used in the patient’s records to describe their symptoms. Huge efforts are made to constrain the vocabulary used in clinicians’ computer systems, but the problem goes deeper than that. It isn’t that we can’t agree on the words: it’s that there aren’t always well-defined concepts to which the words can be attached. In The Promise of Artificial Intelligence, Brian Cantwell Smith tried to explain this by comparing a map of the islands in Georgian Bay in Ontario with an aerial photograph showing the islands along with the underwater topography. On the map, the islands are clearly delineated; in the photograph it’s much harder to say where each island ends and the sea begins, or even exactly how many islands there are. There is a difference between the world as we perceive it, divided into separate objects, and the messier reality. We can use logic to reason about the world as described on the map, but the challenge for AI is how to build the map from the information in the photograph.

Given the extent of the paradigm shift in AI research since 1980, you might think the debate about how to achieve AI had been comprehensively settled in favour of machine learning. But although its algorithms can master specific tasks, they haven’t yet shown anything that approaches the flexibility of human intelligence. It’s worth asking whether there are limits to what machine learning will be capable of, and whether there is something about the way humans think that is essential to real intelligence and not amenable to the kind of computation performed by artificial neural networks. The cognitive scientist Gary Marcus is among the most prominent critics of machine learning as an approach to AI. Rebooting AI, written with Ernest Davis, is a rallying cry to those who still believe in the old religion.

The concept of causality is central to this debate because we are active participants in the world as computers are not. We observe the consequences of our interventions and, from an early age, understand the world in terms of causes and effects. Machine learning algorithms observe correlations among the data provided to them, and can make astonishingly accurate predictions, but they don’t learn causal models and they struggle to distinguish between coincidences and general laws. The question of how to infer causality from observations is, however, an issue not just for AI, but for every science, and social science, that seeks to make inferences from observational rather than experimental data.

This is a question that Judea Pearl has been working on for more than thirty years. During this time, he and his students have, as Dominic Cummings’s eccentric Downing Street job advert put it, ‘transformed the field’. In the 1980s, it seemed to some researchers, including Pearl, that because one characteristic of intelligence was the ability to deal with uncertainty, some of the problems that couldn’t be tackled with logic could possibly be solved using probability. But when it comes to combining large numbers of facts, probability has one huge weakness compared to logic. In logic, complex statements are made up of simpler ones which can be independently proved or disproved. It is harder to deal with complex probabilities. You can’t work out the probability of someone having both heart disease and diabetes from the separate probabilities of their having diabetes or heart disease: you need to know how the likelihood of having one affects the likelihood of having the other. This quantity – the probability of something happening given that something else has already happened – is known as a conditional probability. The main difficulty in using probability is that even a modest increase in the number of concepts to be considered generates an explosive increase in the number of conditional probabilities required.

The original article can be found here.

In the field of AI application with Causality, Professor Judea Pearl is a distinguished pioneer for developing a theory of causal and counterfactual inference based on structural models. In 2011, Professor Pearl won the Turing Award. In 2020, Michael Dukakis Institute also awarded Professor Pearl as World Leader in AI World Society (AIWS.net). At this moment, Professor Judea is a Mentor of AIWS.net and Head of Modern Causal Inference section, which is one of important AIWS.net topics on AI Ethics to develop a positive AI applications for a better world society.

A year ago, none the wiser about what 2020 would bring, I reflected on the pivotal moment that the AI community was in. The previous year, 2018, had seen a series of high-profile automated failures, like self-driving-car crashes and discriminatory recruiting tools. In 2019, the field responded with more talk of AI ethics than ever before. But talk, I said, was not enough. We needed to take tangible actions. Two months later, the coronavirus shut down the world.

In our new socially distanced, remote-everything reality, these conversations about algorithmic harms suddenly came to a head. Systems that had been at the fringe, like HireVue’s face-scanning algorithms and workplace surveillance tools, were going mainstream. Others, like tools to monitor and evaluate students, were spinning up in real time. In August, after a spectacular failure of the UK government to replace in-person exams with an algorithm for university admissions, hundreds of students gathered in London to chant, “Fuck the algorithm.” “This is becoming the battle cry of 2020,” tweeted AI accountability researcher Deb Raji, when a Stanford protestor yelled it again in response to a different debacle a few months later.

At the same time, there was indeed more action. In one major victory, Amazon, Microsoft, and IBM banned or suspended their sale of face recognition to law enforcement, after the killing of George Floyd spurred global protests against police brutality. It was the culmination of two years of fighting by researchers and civil rights activists to demonstrate the ineffective and discriminatory effects of the companies’ technologies. Another change was small yet notable: for the first time ever, NeurIPS, one of the most prominent AI research conferences, required researchers to submit an ethics statement with their papers.

So here we are at the start of 2021, with more public and regulatory attention on AI’s influence than ever before. My New Year’s resolution: Let’s make it count. Here are five hopes that I have for AI in the coming year.

1 – Reduce corporate influence in research

2 – Refocus on common-sense understanding

3 – Empower marginalized researchers

4 – Center the perspectives of impacted communities

5- Codify guard rails into regulation

The original article was published here at the MIT Tech Review.

To support for AI Ethics, Michael Dukakis Institute for Leadership and Innovation (MDI) and Artificial Intelligence World Society (AIWS.net) has developed AIWS Ethics and Practice Index to measure the ethical values and help people achieve well-being and happiness, as well as solve important issues, such as SDGs. Regarding to AI Ethics, AI World Society (AIWS.net) initiated and promoted to design AIWS Ethics framework within four components including transparency, regulation, promotion and implementation for constructive use of AI. In this effort, Michael Dukakis Institute for Leadership and Innovation (MDI) invites participation and collaboration with think tanks, universities, non-profits, firms, and other entities that share its commitment to the constructive and development of full-scale AI for world society.

A PDF of the Report can be found here.