Digital Ethics – Introduction

WATCH:

Created and presented by Bruce Wilson

Reach out if you want to talk about digital & AI ethics in your organization—
Email: e-bruce@manydoors.net
Twitter: @bruce2b
Web: ManyDoors.net

See photo credits below

OVERVIEW:

If you work for an organization that uses data—and just about all organizations do, or will before long—even if your job isn’t specifically about data, your ability to make decisions using data, and decision about data, is becoming more and more important.

Organizations are discovering they need to decide things like

• which problems to solve with data,
• who to hire to solve those problems,
• what kind of training to provide employees,
• what the long term strategy will be, and
• how it is going to explain its data use to the world.

An important subset of these decisions that involves everyone—decisionmakers, employees, and customers alike—falls under the general category of digital ethics, which can encompass how data is collected, stored, used, and shared.

To illustrate, lets look at two examples of digital ethics in action, one surprisingly successful, and one disastrous.

First, the happy story. My friend Aaron Reich is basically the futurist in residence at Avanade, the global technology consulting firm. From the vantage point of his high level insight into many of their consulting projects, last year he called out a few examples where companies achieved remarkable improvement in ways that they can help their customers using data and artificial intelligence. One of these companies is a financial institution in Europe which used AI to predict which customers were likely to “churn”, or leave for a competitor. This was a huge problem for them, and obviously for their customers. By applying machine learning to their customer data, they were able to better understand their customers’ needs, improve their communication, and cut churn in half. This is obviously a win-win for both the company and its customers.

Next, the scary story: in 2015 it became widely known that Volkswagen had “cooked” the emissions test data from millions of its diesel vehicles in order to sell more cars.

• Five days after the story broke, their CEO resigned, and was indicted by the US (but not arrested because there’s no extradition treaty between Gernany and US).  His immediate successor was quickly replaced.

• The CEO of Audi, a division of VW, was eventually arrested for fraud and falsification of documents.

• Relatively few VW personnel who had significant roles in the scheme were also present in the US—and thus subject to US jursidiction. One was an engineer sentenced to 40 months in prison…even though he was just doing what his bosses wanted him to do (which is of course not a defense under the law). Another was an engineering manager who was arrested when he entered the US to vacation in Florida.

• VW set aside $31.7 billion for fines, settlements, recalls and buybacks.

• VW experienced a $66 billion drop in value on the stock market after the fraud was revealed (and continued to underperform the market average for some time)

• VW sales fell in the US.

• A shareholder lawsuit was filed  in Germany seeking $10.4 billion in damages for corporate stock manipulation (failing to promptly disclose its inability to comply with emissions requirements).

• Germany’s national reputation for manufacturing excellence was damaged—as offices of other German car makers were also raided by investigators searching for evidence of possible cheating.

• An engineering company which assisted VW in defeating emissions testing was fined $35 million—this amount was imposed because it was deemed the maximum the contractor could pay without putting it out of business.

• Even though Germany didn’t used to have a provision for what the US calls “class action” lawsuits,  in response to “dieselgate” German lawmakers created a new form of collective legal action that, in November and December 2018 enabled 372,000 German owners of VW cars  to seek compensation for being the victims of this fraud.

What’s the point? Why should ordinary business, government organizations, and non-profits take notice of digital ethics? Most people are unlikely to find themselves in the shoes of the people who successfully reduced churn at the European financial institution, or those who participated in Dieselgate. But many will. And we should all be prepared to find ourselves somewhere on that spectrum. We are increasingly like to discover potential benefits from, and problems with, the ways our organizations use data. We can recommend, and sometimes resist, changes our organizations make. The key is to become more educated, and more fluent, in data and digital ethics. It’s like a muscle—you already have it, but you have to exercise it and train it.

In this series of posts about digital ethics, we’re going to cover issues like:

• What does “ethics” mean—and when is ethics important? Ethics are not clearly defined for many situation, and individual’s views of what is ethical can depend largely on context, (for example, healthcare, politics, or finance), and on individual backgrounds or professions.

• What are potential business gains, and avoidable negative consequences, that can result when organizations develop and apply standards of digital ethics internally?

• Who is responsible for digital ethics? Once again, there is no universal answer to this question, but it’s something that every organization and every individual must be prepared to answer for themselves.

• Who needs to talk to who about digital ethics? And here the answer touches on customer relationships, shareholders, employees, leaders, government, and more.

Please join me as we explore this topic and help make it relevant to everyone—this is definitely not best left exclusively to professors, lawyers, and spin doctors.

Photos used in the video:

Ethan-hoover-422836-unsplash.jpg – Photo by Ethan Hoover on Unsplash

Armando-arauz-318017-unsplash.jpg – Photo by Armando Arauz on Unsplash

Ryan-searle-377260-unsplash.jpg – Photo by Ryan Searle on Unsplash

Robert-haverly-125125-unsplash.jpg – Photo by Robert Haverly on Unsplash

Omer-rana-533347-unsplash.jpg – Photo by Omer Rana on Unsplash

Karolina-maslikhina-503425-unsplash.jpg – Photo by Karolina Maslikhina on Unsplash

Abi-ismail-551176-unsplash.jpg – Photo by abi ismail on Unsplash

Claire-anderson-60670-unsplash.jpg – Photo by Claire Anderson on Unsplash

Rob-curran-396488-unsplash.jpg – Photo by Rob Curran on Unsplash

Rick-tap-110126-unsplash.jpg –Photo by Rick Tap on Unsplash

Chris-liverani-552649-unsplash.jpg – Photo by Chris Liverani on Unsplash

Hedi-benyounes-735849-unsplash.jpg – Photo by Hédi Benyounes on Unsplash

Blind Men Appraising an Elephant by Ohara Donshu (Brooklyn Museum / Wikipedia)

References:

AI/ML success story

Uncovering the ROI in AI by Aaron Reich (Avanade.com)

VW’s Dieselgate

VW engineer sentenced to 40 months in prison for role in emissions cheating by Megan Geuss (ArsTechnica)

Five things to know about VW’s ‘dieselgate’ scandal (Phys.org)

$10.4-billion lawsuit over diesel emissions scandal opens against Volkswagen (Bloomberg / LA Times)

How VW Paid $25 Billion for ‘Dieselgate’ — and Got Off Easy (Fortune / Pro Publica)

VW Dieselgate scandal ensnares German supplier, to pay $35M fine by Nora Naughton
(The Detroit News)

Car sales suffer second year of gloom by Alan Tovey & Sophie Christie (Telegraph UK)

Nearly 375,000 German drivers join legal action against Volkswagen (Business Day)

 

Who Needs Reasons for AI-Based Decisions?

Deep learning systems, which are the most headline-grabbing examples of the AI revolution—beating the best human chess and poker players, self-driving cars, etc.—impress us so very much in part because they are inscrutable. Not even the designers of these systems know exactly why they make the decisions they make. We only know that they are capable of being highly accurate…on average.

Meanwhile, software companies are developing complex systems for business and government that rely on “secret sauce” proprietary data and AI models. In order to protect their intellectual property rights, and profitability, the developers of these systems typically decline to reveal how exactly their systems work. This gives rise to a tradeoff between profit motive, which enables rapid innovation (something government in particular isn’t known for), and transparency, which enables detection and correction of mistakes and biases. And mistakes do occur…on average.

pay no attention to the man behind the curtain
Photo by Andrew Worley on Unsplash

On the one hand, a lack of transparency in deep learning and proprietary AI models has led to criticism from a number of sources. Organizations like AI Now  and ProPublica are surfacing circumstances where a lack of transparency leads to abuses such as discriminatory bias. The EU has instituted regulations (namely GDPR) that guarantee its citizens the right to an appeal to a human being when AI-based decisions are being made. And, last but not least, there is growing awareness that AI systems—including autonomous driving and health care systems—can be invisibly manipulated by those with a motive like fraud or simple mischief. Continue reading “Who Needs Reasons for AI-Based Decisions?”

Are marketers becoming more like drivers and less like spectators?

This morning I had coffee with Tejas Dixit of Market Dialogues who showed me Junction, his new SaaS social media management solution.

Junction is designed to help small and medium sized businesses plan, execute, and manage their social media initiatives effectively. Unlike many social media management solutions which offer publishing to social media accounts, monitoring conversations, and analytics as independent solutions, or as siloed components, Junction tightly integrates and dashboards all three.

I’ve noticed that a major stumbling block of many social media management solutions is that the feedback they offer about the success (or lack thereof) of social media efforts can be difficult to act on. Even when publishing, monitoring, and analytics are available under the same login, the gap between action and feedback can be wide enough to leave a major hurdle in the path of social media marketers. Meanwhile, the level of complexity conveyed by analytics tools can leave marketers, and the people they are accountable to, bewildered.

Continue reading “Are marketers becoming more like drivers and less like spectators?”

3 excellent books about how people make decisions

On the recommendation of a Twitter friend I recently read (or, rather, listened to the audio editions of) three excellent books about how people make decisions:

The Art of Choosing by Sheena Iyengar
Predictably Irrational by Dan Ariely
How We Decide by Jonah Lehrer

All three contain countless nuggets of recent scientific insight into behavioral economics, or why people and markets behave as we do, as explained by three very cogent thinkers. All three focused on defining the abilities, strengths and weaknesses of different brain areas; how human impulses mesh and are sorted and acted on; predictable biases of both “rational” and “emotional” sorts; and, what we can do to avoid—and manipulate—biases and errors. Interestingly, all three authors acknowledged the increasing difficulty academics are having in drawing sharp lines between “rational” and “emotional” behavior when confronted with contemporary knowledge about brain function, but all three attempted to draw distinctions between “rational” and “emotional” decisions nonetheless—with varying degrees of success.

Playing poker
Playing poker well involves combining “rational” and “emotional” decisions and knowing when to do which.

The book I enjoyed the most was Jonah Lehrer’s, which I could oversimplify by describing as “neuroscience discovers B.F. Skinner” because of his focus on learned behavior. But perhaps that’s because Lehrer’s approach best fit my personal preconceptions about behavior—and the fact that B.F. Skinner was still working at the psych department where I received my undergraduate degree in psychology way back when I was in school.

Ariely’s book is premised on the idea that traditional economic theory is Continue reading “3 excellent books about how people make decisions”