Towards Responsible AI (Part 3)

Three Lines of Defense for AI Governance

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Source: Photo by Anton Polidovets on Unsplash

Responsible AI is a broad topic covering multiple dimensions of the socio-technical system called Artificial Intelligence. We refer to AI as a socio-technical system here as it captures the interaction between humans and how we interact with AI. In the first part of this series we looked at AI risks from five dimensions. In the second part of this series we look at the ten principles of Responsible AI for corporates.

In this article we dive into AI Governance — what do we really mean by governance? What does AI governance entail? …

Towards Responsible AI (Part 2)

First step towards operationalizing AI ethics

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Photo by Jason Leung on Unsplash

In the first part of this series, we looked at AI risks from five dimensions. We talked about the dark side of AI, without really going into how we would manage and mitigate these risks. In this and subsequent articles, we will look at how to exploit the benefits of AI, while at the same time guarding against the risks.

A quick plot of search trends shows that the words “AI Ethics”, “Ethical AI”, “Beneficial AI”, “Trustworthy AI”, and “Responsible AI” started becoming extremely popular over the past five years. In my (first author’s) early exploits of AI in the ’80s and ‘90s talking about ethics was relegated to a small fringe of academics and definitely not a topic of conversation in the business world — at least with respect to AI ethics. …

TOWARDS RESPONSIBLE AI (PART 1)

Get started on your journey towards Responsible AI

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Source: Photo by Loic Leray on Unsplash

Thirty years from now, will we look back at 2020 as the year when AI discriminated against minority groups, disinformation propagated by special interest groups and aided by AI-based personalization caused political instability, deep fakes and other AI-supported security infringements basically rendered AI untrustworthy and propelled us into yet another AI winter, or will we look upon 2020 as the year that provided the impetus for the world bodies, corporates, and individuals to come together to ban autonomous weapons systems, assess, monitor, and govern sensitive AI technologies like deep fakes, facial recognition systems, and other sensitive technologies and truly create reliable, robust, and responsible AI beneficial to all humans? …

DATA SCIENTISTS ARE FROM MARS AND SOFTWARE ENGINEERS ARE FROM VENUS (PART 5)

Agile Software 2.0 Manifesto and Process

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Source: Photo by Robert Collins on Unsplash

In Part 1 of this series, we examined the key differences between software and models; in Part 2, we explored the twelve traps of conflating models with software; in Part 3, we looked at the evolution of models; and in Part 4, we went through the model lifecycle. Now, in our final part of the series, we address how the model lifecycle and the agile software development methodology should come together.

Based on our previous discussions, we are primarily concerned with how the model lifecycle process — with its iterative value discovery, value delivery and value stewardship — can be combined with the traditional agile software development methodology. The emphasis of this article is on the combination of the two methodologies; it is not about making data science or model lifecycle agile. …

Getting started on your Responsible AI journey

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Source: Image from John Moeses Bauan from Unsplash

In a recent conference on Responsible AI for Social Empowerment (RAISE), held in India, the topic of discussion was on explainable AI. Explainable AI is a critical element of the broader discipline of responsible AI. Responsible AI encompasses ethics, regulations, and governance across a range of risks and issues related to AI including bias, transparency, explicability, interpretability, robustness, safety, security, and privacy.

Interpretability and explainability are closely related topics. Interpretability is at the model level with an objective of understanding the decisions or predictions of the overall model. Explainability is at an individual instance of the model with the objective of understanding why the specific decision or prediction was made by the model. When it comes to explainable AI we need to consider five key questions — Whom to explain? Why explain? When to explain? How to explain? …

Panel Discussion in the conference on Responsible AI for Social Empowerment (RAISE) India, 2020

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Source: Photos from Tingey Injury Law Firm, Diego PH, and Sharon McCutcheon in Unsplash

In a recent conference on Responsible AI for Social Empowerment (RAISE), held in India, the topic of discussion was on balancing regulation and innovation. On the one hand we have some industry leaders and professional groups urging policy makers not to regulate AI systems too soon; while on the other hand some civil liberty groups, joined by other companies urging policy makers to outright ban certain types of AI systems and regulate the rest. …

COMPETITIVE ADVANTAGE FROM THE FOUR WAVES OF DIGITAL (PART 2)

Getting started on your Automation, Analytics, and AI strategy

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Source: This robot solve a Rubik’s cube in world record time, Cubastic, YouTube

In the first part of the Digital Revolution series, I looked at how data, automation, analytics, and AI are four inseparable parts of the revolution — the unbeatable quartet. While all of us can enjoy the music of the unbeatable quartet, most of us would be challenged to create one. Many of us would not know where to start.

The situation is not too dissimilar when it comes to building your competitive advantage using data, automation, analytics and AI. We have seen organizations that have developed their big data strategy, created a data lake and are now searching for some good business use cases. There are others who have focused on RPA, found some good early successes, but are now struggling with a ‘bot’ management problem. Some others have siloed analytics and automation Centers of Excellence that are competing to expand into AI. …

DATA SCIENTISTS ARE FROM MARS AND SOFTWARE ENGINEERS ARE FROM VENUS (PART 4)

Value scoping, discovery, delivery, and stewardship

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Created by Authors based on Youtube video Monarch Butterfly Metamorphosis time-lapse FYV 1080 HD

In Part 1 of this series we examined the key differences between software and models; in Part 2 we explored the twelve traps of conflating models with software; and in Part 3 we looked at the evolution of models. In this article, we go through the model lifecycle, from the initial conception of the idea to build models to finally delivering the value from these models.

We breakdown the entire lifecycle of models into four major phases — scoping, discovery, delivery, and stewardship. While there are many similarities between this model lifecycle and a typical software lifecycle, there are significant differences as well, stemming from the differences between software and models that we started this series with. …

Competitive Advantage from the Four Waves of Digital (4wD) (Part 1)

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Image by Author

The business world has been bombarded with one revolution after another. The Digital Revolution has become the catchphrase and every organization has already been through a digital transformation or is currently going through one. However, the word digital revolution or digital transformation can mean many things to many people. Digital revolution is a broad term that encompasses all the advances in information technology since the introduction of computers in the late half of the twentieth century.

In a series of articles, I will look at the four inseparable parts of the digital revolution — the star performers of an unbeatable quartet. Automation and Analytics are the two violins of the string quartet — providing the efficiency benefits and effectiveness benefits, respectively. Data is the viola of the quartet and integral to the harmony and often accompanies both automation and analytics. Artificial Intelligence is the cello of the quartet, the closest to the human vocal range, that integrates the other three aspects. All four are critical to create a competitive advantage. But we have seen many companies treat them independently or in pairs and not really appreciate the beautiful music of the quartet. For the ocean lover we equate these to the four waves of digital. For the outdoor enthusiast this becomes 4wD (4 waves of Digital). Whatever analogy suits you best — the key message we want to drive home is that these four must be together and as an integral component of your competitive advantage. …

DATA SCIENTISTS ARE FROM MARS AND SOFTWARE ENGINEERS ARE FROM VENUS (PART 3)

From Data Scientists on Mars to Software Engineers on Venus to ModelOps on Earth

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Emerging Roles at the intersection of Software, Models, and Data

In Part 1 of this series we examined the key differences between software and models and in Part 2 we explored the twelve traps of conflating models with software. Both these articles were focused on highlighting the issues, but did not provide any solutions. In the next couple of articles we will focus on providing some concrete practices for addressing these gaps.

The potential contribution of AI to the global economy and the importance of investing in AI is well recognized within the business and technical communities. In a recent CEO survey, more than 85% of the CEO’s believed that AI will significantly change the way they do business. Although only 6% of those surveyed admitted to having enterprise-wide AI initiatives, nearly 20% of them plan to deploy AI enterprise-wide in the near-term. One of the biggest challenges in enterprise-wide deployment of models is the time it takes to deploy models. …

About

AnandSRao

Global AI lead for PwC; Researching, building, and advising clients on AI. Focused at the intersection of AI innovation, policy, economics and application.

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