How to Start Your Organization’s Data and AI Ethics Program

Introduction to a 4 Part Series

Let’s suppose your organization (or some part thereof) has decided to take a more principled approach towards the data and/or algorithms it uses by establishing ethics-based ground rules for their use. Maybe this stems from concerns expressed by leadership, legal counsel, shareholders, customers, or employees about potential harms arising from a technology you’re already using or about to use. Maybe it’s because you know companies like Google, Microsoft, and Salesforce have already taken significant steps to incorporate data and AI ethics requirements into their business processes.

ethics principles

Photo by Kelly Sikkema on Unsplash

Regardless of the immediate focus, keep in mind that you probably don’t need to launch the world’s best program on day one (or year one). The bad news is that there is no plug and play, one-size-fits-all solution awaiting you. You and your colleagues will need to begin by understanding where you are now, visualizing where you are headed, and incrementally building a roadway that takes you in the right direction. In fact, it makes sense to start small—like you would when prototyping a new product or line of business—learning and building support systems as you go. Over time, your data and AI ethics program will generate long term benefits, as AI and data ethics increasingly become important for every organization’s good reputation, growth in value, and risk management.

In the following 4 part series about initiating a functional data and AI ethics program we will cover the basic steps you and your team will need to undertake, including:

Part 1: Recruit a Task Force to Build a Data and AI Ethics Program

Part 2: Educate Your Organization About Data and AI Ethics

Part 3: Create a Map of Potential Data and AI Ethics Hot Spots

Part 4: Test Your Data and AI Ethics Program

 

Next – Part 1: Recruit a Task Force to Build a Data and AI Ethics Program

What Warren Buffett said about Ethics

I recently read Warren Buffett’s authorized biography The Snowball. It was a chewy read, at just under 900 pages with copious footnotes and fine grained details about what he ate, parties he attended, and vacations he took, in addition to background profiles of many of the companies he bought and larger-than-life personalities he associated with.

The bits I found most interesting concerned Buffett’s concerns about corporate ethics. In general he sought to put his money behind individuals he felt he could trust, not only because he believed they could make money, but because of their ethics in business dealings. Of course, he didn’t always choose well. And sometimes he compromised—and later regretted certain choices. Continue reading “What Warren Buffett said about Ethics”

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)

 

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”

If you can lead, you can be effective in social media

Trading fives - in Jazz leadership moves aroundI recently participated in an in-person discussion about leadership attended by a number of people I know through social media. Because the instigators of the discussion, Pam Hoelzle and Ethan Yarbrough, publicized the event online, the composition of the group and the conversation itself were flavored by a social media perspective. The discussion delivered several valuable takeaways, but one idea that stood out because it was useful and a little counterintuitive was this: Effective social media participation is like effective leadership.

By “effective social media participation” I mean purposefully interacting with people via Twitter, blogs, Facebook, LinkedIn, and other forms of social media to find people and information we need to accomplish business and personal goals.

By “effective leadership” I mean motivating high performing people to work towards a common purpose, whether or not we are are the “manager” of those folks (one can lead by influence even when one isn’t in a position of authority).

During our discussion three common threads came out that connect effective social media participation and effective leadership: selection, reciprocation, and vision.

Selection. In both social media and leadership we benefit by choosing our contributors carefully.

In the context of social media I often call this “filtering”. A LOT of potentially useful information is Tweeted, blogged, Buzzed, or otherwise published by people analyzing (or simply regurgitating) what they discover. In fact, there is so much of this information, and meta-information, there isn’t nearly enough time to skim it all efficiently much less read it all. After participating in social media for a time–if it wasn’t obvious to us from the beginning–most of us recognize that just because someone Tweets brilliantly and has a large following doesn’t mean we’ll find the time to read their stuff very often (sorry, @StephenFry). Social media is best managed like a lavish all-you-can-eat buffet: even something that looks very tasty won’t make it onto our plates if taking it would require sacrificing something we desire even more. In social media an information source must be consistently relevant and efficient for our purposes to be useful, not just beautiful in our sight.

Similarly, in a leadership context an impressive resume is just a starting point when determining whether there’s a good fit between a potential team member and a position on our team. Which is why personal relationships and recommendations from people we trust are so valuable when recruiting team members–and when choosing social media sources. One must be selective to be effective.

Delegation is the powerful outcome of good contributor selection in both social media and leadership. Whether social media networks or work groups, ideally we implicitly trust our teams to produce quality results for us. Otherwise we’re tempted to second guess our contributors, which deprives them of the rewards of our recognition, duplicates effort, and leads us down the path of information overload. Like we trust the curator of an art gallery to collect and display a worthy collection or art, like we trust the editors of our favorite publications to discover and accurately portray stories for us, like we trust our auto mechanics to keep our cars running, we should trust our teams to do their jobs. If and when we don’t feel we can rely on our team, whether we’re working as a leader or as a participant in social media, that’s a not-so-subtle sign that we will benefit from improving our approach towards selection, reciprocation, and vision.

Reciprocation. Both social media and leadership require reciprocation to be sustainable. Other people contribute to our successes, and we contribute to theirs. That’s the nature of the bargain. The biggest mistake I see would-be social media “power users” and would-be leaders make is not focusing enough on what success looks like for their team members. A true leader (as distinguished from a “manager”) provides team members with what they value above and beyond their pay checks, for example, by encouraging them to take responsibilities that will help them develop personally and professionally. And just as leadership requires more than a checkbook and a list of instructions for employees to follow, social media mastery requires more than pumping out branded messages to subscribers. Social media rock stars listen to what is said in their networks, recognize needs, and respond by offering referrals, links, analysis, or whatever else they have to help meet those needs.

Vision. Last but not least: to be effective in either social media or leadership we must communicate a clear, consistent vision that lets people know what we want them to contribute, and thus (directly or indirectly) what they will be rewarded for contributing. If we can’t sustain the insight we need to define and communicate our vision we’ll have a difficult time selecting people who can contribute to it, and neither we nor our team members will be particularly good at providing what the other needs.

One final thought. As a leader, or as a participant in social media, we get out what we put in. Just as putting time, focus and energy into leadership is essential to be an effective leader, putting time, focus and energy into social media is essential to be effective in social media. Those of us who believe leadership or social media are among our core responsibilities are thus obligated to make studying and practicing our craft a high priority for so long as we wish to be effective.

Thanks to everyone who participated in the conversation, including but not limited to: Moderators @pamhoelzle and @Ethany; graphic interpreter @pdobrowolski; hosts @petechee and @alyssamag; and @jdkovarik, @colleencar, @ShaunaCausey, @coolguygreg, @cherylnichols, @RJHSeattle, @pmcmortgages, @blainemillet, and @shannonevans (there were other folks not mentioned here but I don’t have their details). The opinions in this post are my own, and these folks may or may not agree with what is written here, but either way I benefited from their contributions.

A 1 page (2 sided) consensus “cheat sheet”

My previous post describes the benefits and limitations of the five-degree consensus process that I recommend to clients who use consensus decision making as part of their repertoire of business skills.

In this entry I offer you a downloadable chart plus a condensed, one-page explanation of how to use a consensus scale which you may want to print out for your own use or e-mail to friends and co-workers for their use. (If you’re really hard core, print the chart on special white-board paper for laser printers. Then you can mark and erase right on it as much as you want.)

DOWNLOAD IT HERE: > Using a five-degree consensus scale to reach consensus: the cheat sheet (in PDF Acrobat format)

To download it to your computer, right-click with your mouse (or on a Mac, option-click).

When, again, is a consensus process particularly appropriate? See my post from December 8, 2005 for a more detailed answer to this question. In general, a consensus process may be valuable when:

  • you want a proposal examined carefully. A consensus process pushes people proposing a course of action to clarify their reasoning and pushes others to wrap their minds around the proposal, encouraging everyone to understand it, ask questions, and offer input.
  • you fear weak follow-through, and thus you want to secure support up front or quit before setting a decision up for failure. A consensus process pushes everyone in a group to assume responsibility for a decision, including follow-through down the road.
  • you aren’t in a desperate hurry. Although a rapid decision may be reached by consensus, for speed alone you’re frequently better off assigning a qualified solo decision maker.

A simple consensus building process

A simple consensus process can reveal whether the members of a group agree about a proposed course of action while promoting discussion that can lead to agreement.

Polling a group using a five degree consensus scale “takes the temperature” of a group, instantly demonstrating when a proposal requires no further consideration either because it already has universal support or because opposition is overwhelming. When consensus for or against a proposal does not already exist, the scale identifies whose concerns need to be addressed and their degree of difference from others in the group, so that an effort can be made to close the gap or abandon the attempt to reach consensus.

Productive discussion is encouraged because it’s easy and acceptable for group members to express uncertainties, differences of opinion, and alternative approaches without appearing hostile, disruptive, or uncooperative towards the group or the group’s leader. Consensus is not a foregone conclusion using the scale, but the give-and-take atmosphere it facilitates helps with obtaining buy-in, discovering new options and changes in the plan, and enabling movement towards or away from support for a proposal.

Many consensus scales are in use utilizing hand gestures, cards, colors, or numerical tallies. The simplest might be the three-degrees scale such as “hot, neutral, cold” or “yes, maybe, no,” or “go, caution, stop,” but I find that a slightly wider range is useful in most cases. The following is a five-point scale I have adapted from a system sometimes called “shades of consensus” or “levels of consensus.”

After a plan of action has been proposed, each participant in the decision chooses a number from one to five to signal their degree of support. These numbers signal roughly the following:

      1: Yes. Let’s do it.
      2: OK. It’s good enough.
      3: Maybe. I have questions.
      4: Wait. Can we change it?
      5: No. Let’s do something else.

After everyone has weighed-in, all ones and twos show consensus support for a plan, although time might be well spent clarifying what, if anything, could be changed to bring twos up to ones. All fours and fives shows consensus opposition to a plan, although discussion may still be useful to generate a shared sense of why a proposal was rejected and to spur thinking about alternatives. Threes suggest more explanation is needed.

Some number of ones or twos alongside fours or fives demonstrates a lack of clear consensus and need for further discussion or in-depth exploration of options, if consensus remains the group’s goal. Polling a group with a consensus scale is an iterative process, which is to say, multiple polls can be taken to discover movement in consensus rankings, or lack thereof, after discussion.

It’s worth emphasizing that the whole point of this is to walk through the process, not to achieve a pre-determined outcome. What is more, deadlock is an entirely acceptable result using this technique. Using a consensus scale does not guarantee that a particular proposal will ultimately receive either consensus support or opposition. A strong contrary position taken by even one participant is enough to deny “consensus decision” status – but of course, there are always alternative proposals, and alternative ways to arrive at decisions besides consensus.

When a group is deadlocked, the value of a consensus process is that it reveals the existence of the deadlock and, hopefully, the reasons for it. Typically this leads to a new proposals which address the concerns on both sides of the consensus chart in a way which unifies everyone.

If a consensus can’t be reached, but a decision must be made regardless, it may become necessary to abandon the effort to reach consensus and to use another decision-making style instead. For example, if a board must arrive at a certain decision within a certain time frame, a failure to reach consensus may mean that a simple majority vote will be required instead. Or in a business group, it may become necessary for the senior person in the hierarchy to make an executive decision, delegate, or otherwise choose a different course for decision making. Either way, a group should begin a consensus decision-making process knowing the consequences it will face for failing to come to a decision, whether that means accepting responsibility for no decision being delivered or understanding that the decision will pass out of their hands and on to another process or person.

Regardless of the ultimate result, a consensus scale makes it a no-brainer for a diverse group of people to express and develop individual levels of understanding and enthusiasm, while making it easy for leaders to gauge the support a proposal will receive if it is adopted.