Responding Party’s Complaints of Financial Burden of Document Review Were Unsupported by the Evidence, Any Evidence

August 5, 2018

One of the largest cases in the U.S. today is a consolidated group of price-fixing cases in District Court in Chicago. In Re Broiler Chicken Antitrust Litigation, 290 F. Supp. 3d 772 (N.D. Ill. 2017) (order denying motions to dismiss and discussing the case). The consolidated antitrust cases involve allegations of a wide spread chicken price-fixing. Big Food Versus Big Chicken: Lawsuits Allege Processors Conspired To Fix Bird Prices (NPR 2/6/18).

The level of sales and potential damages are high. For instance, in 2014 the sales of broiler chickens in the U.S. was $32.7 Billion. That’s sales for one year. The classes have not been certified yet, but discovery is underway in the consolidated cases.

The Broiler Chicken case is not only big money, but big e-discovery. A Special Master (Maura Grossman) was appointed months ago and she developed a unique e-discovery validation protocol order for the case. See: TAR for Smart Chickens, by John Tredennick and Jeremy Pickens that analyzes the validation protocol.

Maura was not involved in the latest discovery dispute where, Agri Stats, one of many defendants, claimed a request for production was too burdensome as to it. The latest problem went straight to the presiding Magistrate Judge Jeffrey T. Gilbert who issued his order on July 26, 2018. In re Broiler Chicken Antitrust Litig., 2018 WL 3586183 (N.D. Ill. 7/26/18).

Agri Stats had moved for a protective order to limit an email production request. Agri Stats claimed that the burden imposed was not proportional because it would be too expensive. Its lawyers told Judge Gilbert that it would cost between $1,200,000 and $1,700,00 to review the email using the keywords negotiated.

Fantasy Hearing

I assume that there were hearings and attorney conferences before the hearings. But I do not know that for sure. I have not seen a transcript of the hearings with Judge Gilbert. All we know is that defense counsel told the judge that under the keywords selected the document review would cost between $1,200,000 and $1,700,000, and that they had no explanation on how the cost estimate was prepared, nor any specifics as to what it covered. Although I was not there, after four decades of doing this sort of work, I have a pretty good idea of what was or might have been said at the hearing.

This representation of million dollar costs by defense counsel would have gotten the attention of the judge. He would naturally have wanted to know how the cost range was calculated. I can almost hear the judge say from the bench: “$1.7 Million Dollars to do a doc review. Yeah, ok. That is a lot of money. Why so much counsel? Anyone?” To which the defense attorneys said in response, much like the students in Ferris Beuller’s class:

“. . . . . .”

 

Yes. That’s right. They had Nothing. Just Voodoo Economics

Well, Judge Gilbert’s short opinion makes it seem that way. In re Broiler Chicken Antitrust Litig., 2018 WL 3586183 (N.D. Ill. 7/26/18).

If a Q&A interchange like this happened, either in a phone hearing, or in person, then the lawyers must have said something. You do not just ignore a question by a federal judge. The defense attorneys probably did a little hemming and hawing, conferred among themselves, and then said something to the judge like: “We are not sure how those numbers were derived, $1.2M to $1.5M, and will have to get back to you on that question, Your Honor.” And then, they never did. I have seen this kind of thing a few times before. We all try to avoid it. But it is even worse to make up a false story, or even present an unverified story to the judge. Better to say nothing and get back to the judge with accurate information.

Discovery Order of July 26, 2018

Here is a quote from Judge Gilbert’s Order so you can read for yourself the many questions the moving party left unanswered (detailed citations to record removed; graphics added):

Agri Stats represents that the estimated cost to run the custodial searches EUCPs propose and to review and produce the ESI is approximately $1.2 to $1.7 million. This estimated cost, however, is not itemized nor broken down for the Court to understand how it was calculated. For example, is it $1.2 to $1.7 million to review all the custodial documents from 2007 through 2016? Or does this estimate isolate only the pre-October 2012 custodial searches that Agri Stats does not want to have to redo, in its words? More importantly, Agri Stats also admits that this estimate is based on EUCPs’ original proposed list of search terms. But EUCPs represent (and Agri Stats does not disagree) that during their apparently ongoing discussions, EUCPs have proposed to relieve Agri Stats of the obligation to produce various categories of documents and data, and to revise the search terms to be applied to data that is subject to search. Agri Stats does not appear to have provided a revised cost estimate since EUCPs agreed to exclude certain categories of documents and information and revised their search terms. Rather, Agri Stats takes the position that custodial searches before October 3, 2012 are not proportional to the needs of the case — full stop — so it apparently has not fully analyzed the cost impact of EUCPs’ revised search terms or narrowed document and data categories.

The Court wonders what the cost estimate is now after EUCPs have proposed to narrow the scope of what they are asking Agri Stats to do. (emphasis added) EUCPs say they already have agreed, or are working towards agreement, that 2.5 million documents might be excluded from Agri Stats’s review. That leaves approximately 520,000 documents that remain to be reviewed. In addition, EUCPs say they have provided to Agri Stats revised search terms, but Agri Stats has not responded. Agri Stats says nothing about this in its reply memorandum.

EUCPs contend that Agri Stats’s claims of burden and cost are vastly overstated. The Court tends to agree with EUCPs on this record. It is not clear what it would cost in either time or money to review and produce the custodial ESI now being sought by EUCPs for the entire discovery period set forth in the ESI Protocol or even for the pre-October 3, 2102 period. It seems that Agri Stats itself also does not know for sure what it would have to do and how much it would cost because the parties have not finished that discussion. Because EUCPs say they are continuing to work with Agri Stats to reduce what it must do to comply with their discovery requests, the incremental burden on what Agri Stats now is being asked to do is not clear.

For all these reasons, Agri Stats falls woefully short of satisfying its obligation to show that the information [*10] EUCPs are seeking is not reasonably accessible because of undue burden or cost.

Estimations for Fun and Profit

In order to obtain a protective order you need to estimate the costs that will likely be involved in the discovery from which you seek protection. Simple. Moreover, it obviously has to be a reasonable estimate, a good faith estimate, supported by the facts. The Brolier Chicken defendant, Agri Stats, came up with an estimate. They got that part right. But then they stopped. You never do that. You do not just throw up a number and hope for the best. You have to explain how it was derived. Blushing at any price higher than that is not a reasonable explanation, but is often honest.

Be ready to explain how you came up with the cost estimate. To break down the total into its component parts and allow the “Court to understand how it was calculated.” Agri Stats did not do that. Instead, they just used a cost estimate of between $1.2 to $1.7 million. So of course Agri Stats’ motion for protective order was denied. The judge had no choice because no evidence to support the motion was presented, neither factual or expert evidence. There was no need for Judge Gilbert to go into the secondary questions of whether expert testimony was also needed and whether it should be under Rule 702. He got nothing remember. No explanation for the $1.7 Million.

The lesson of the latest discovery order in Broiler Chicken is pretty simple. In re Broiler Chicken Antitrust Litig., 2018 WL 3586183 (N.D. Ill. 7/26/18). Get a real cost estimate from an expert. The expert needs to know and understand document review, search and costs of review. They need to know how to make reasonable search and retrieval efforts. They also need to know how to make reliable estimates. You may need two experts for this, as not all have expertise in both fields, but they are readily available. Many can even talk pretty well too, but not all! Seriously, everybody knows we are the most fun and interesting lawyer subgroup.

The last thing to do is skimp on an expert and just pull out a number from your hat (or your vendor’s hat) and hope for the best.

This is federal court, not a political rally. You do not make bald assertions and leave the court wondering. Facts matter. Back of the envelope type guesses are not sufficient, especially in a big case like Broiler Chicken. Neither are guesstimates by people who do not know what they are doing. Make disclosure and cooperate with the requesting party to reach agreement. Do not just rush to the courthouse hoping to  dazzle with smoke and mirrors. Bring in the experts. They may not dazzle, but they can get you beyond the magic mirrors.

Case Law Background

Judge Paul S. Grewal, who is now Deputy G.C. of Facebook, said quoting The Sedona Conference in Vasudevan: There is no magic to the science of search and retrieval: only mathematics, linguistics, and hard work.Vasudevan Software, Inc. v. Microstrategy Inc., No. 11-cv-06637-RS-PSG, 2012 US Dist LEXIS 163654 (ND Cal Nov 15, 2012) (quoting The Sedona Conference, Best Practices Commentary on the Use of Search and Information and Retrieval Methods in E-Discovery, 8 Sedona Conf. J. 189, 208 (2007). There is also no magic to the art of estimation, no magic to calculating the likely range of cost to search and retrieve the documents requested. Judge Grewal refused to make any decision in Vasudevan without expert assistance, recognizing that this area is “fraught with traps for the unwary” and should not be decided on mere arguments of counsel.

Judge Grewal did not address the procedural issue of whether Rule 702 should govern. But he did cite to Judge Facciola’s case on the subject, United States v. O’Keefe, 537 F. Supp. 2d 14 (D.D.C. 2008). Here Judge Facciola first raised the discovery expert evidence issue. He not only opined that experts should be used, but that the parties should follow the formalities of Evidence Rule 702. That governs things such as whether you should qualify and swear in an expert and follow otherwise follow Rule 702 on their testimony. I discussed this somewhat in my earlier article this year, Judge Goes Where Angels Fear To Tread: Tells the Parties What Keyword Searches to Use.

Judge Facciola in O’Keffe held that document review issues require expert input and that this input should be provided with all of the protections provided by Evidence Rule 702.

Given this complexity, for lawyers and judges to dare opine that a certain search term or terms would be more likely to produce information than the terms that were used is truly to go where angels fear to tread. This topic is clearly beyond the ken of a layman and requires that any such conclusion be based on evidence that, for example, meets the criteria of Rule 702 of the Federal Rules of Evidence. Accordingly, if defendants are going to contend that the search terms used by the government were insufficient, they will have to specifically so contend in a motion to compel and their contention must be based on evidence that meets the requirements of Rule 702 of the Federal Rules of Evidence.

Conclusion

In the Boiler Chicken Antitrust Order of July 27, 2018, a motion for protective order was denied because of inadequate evidence of burden. All the responding party did was quote a price-range, a number presumably provided by an expert, but there was no explanation. More evidence was needed, both expert and fact. I agree that generally document review cost estimation requires opinions of experts. The experts need to be proficient in two fields. They need to know and understand the science of document search and retrieval and the likely costs for these services for a particular set of data.

Although all of the formalities and expense of compliance with Evidence Rule 702 may be needed in some cases, it is probably not necessary in most. Just bring your expert to the attorney conference or hearing. Yes, two experts may well disagree on some things, probably will, but the areas of agreement are usually far more important. That in turn makes compromise and negotiation far easier. Better leave the technical details to the experts to sort out. That follows the Rule 1 prime directive of “just, speedy and inexpensive.” Keep the trial lawyers out of it. They should instead focus and argue on what the documents mean.

 

 

 


Ethical Guidelines for Artificial Intelligence Research

November 7, 2017

The most complete set of AI ethics developed to date, the twenty-three Asilomar Principles, was created by the Future of Life Institute in early 2017 at their Asilomar Conference. Ninety percent or more of the attendees at the conference had to agree upon a principle for it to be accepted. The first five of the agreed-upon principles pertain to AI research issues.

Although all twenty-three principles are important, the research issues are especially time sensitive. That is because AI research is already well underway by hundreds, if not thousands of different groups. There is a current compelling need to have some general guidelines in place for this research. AI Ethics Work Should Begin Now. We still have a little time to develop guidelines for the advanced AI products and services expected in the near future, but as to research, the train has already left the station.

Asilomar Research Principles

Other groups are concerned with AI ethics and regulation, including research guidelines. See the Draft Principles page of AI-Ethics.com which lists principles from six different groups. The five draft principles developed by Asilomar are, however, a good place to start examining the regulation needed for research.

Research Issues

1) Research Goal: The goal of AI research should be to create not undirected intelligence, but beneficial intelligence.

2) Research Funding: Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies, such as:

  • How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked?
  • How can we grow our prosperity through automation while maintaining people’s resources and purpose?
  • How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI?
  • What set of values should AI be aligned with, and what legal and ethical status should it have?

3) Science-Policy Link: There should be constructive and healthy exchange between AI researchers and policy-makers.

4) Research Culture: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI.

5) Race Avoidance: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards.

Principle One: Research Goal

The proposed first principle is good, but the wording? Not so much. The goal of AI research should be to create not undirected intelligence, but beneficial intelligence. This is a double-negative English language mishmash that only an engineer could love. Here is one way this principle could be better articulated:

Research Goal: The goal of AI research should be the creation of beneficial intelligence, not  undirected intelligence.

Researchers should develop intelligence that is beneficial for all of mankind. The Institute of Electrical and Electronics Engineers (IEEE) first general principle is entitled “Human Benefit.” The Asilomar first principle is slightly different. It does not really say human benefit. Instead it refers to beneficial intelligence. I think the intent is to be more inclusive, to include all life on earth, all of earth. Although IEEE has that covered too in their background statement of purpose to “Prioritize the maximum benefit to humanity and the natural environment.”

Pure research, where raw intelligence is created just for the hell of it, with no intended helpful “direction” of any kind, should be avoided. Because we can is not a valid goal. Pure, raw intelligence, with neither good intent, nor bad, is not the goal here. The research goal is beneficial intelligence. Asilomar is saying that Undirected intelligence is unethical and should be avoided. Social values must be built into the intelligence. This is subtle, but important.

The restriction to beneficial intelligence is somewhat controversial, but the other side of this first principle is not. Namely, that research should not be conducted to create intelligence that is hostile to humans.  No one favors detrimental, evil intelligence. So, for example, the enslavement of humanity by Terminator AIs is not an acceptable research goal. I don’t care how bad you think our current political climate is.

To be slightly more realistic, if you have a secret research goal of taking over the world, such as  Max Tegmark imagines in The Tale of the Omega Team in his book, Life 3.0, and we find out, we will shut you down (or try to). Even if it is all peaceful and well-meaning, and no one gets hurt, as Max visualizes, plotting world domination by machines is not a positive value. If you get caught researching how to do that, some of the more creative prosecuting lawyers around will find a way to send you to jail. We have all seen the cheesy movies, and so have the juries, so do not tempt us.

Keep a positive, pro-humans, pro-Earth, pro-freedom goal for your research. I do not doubt that we will someday have AI smarter than our existing world leaders, perhaps sooner than many expect, but that does not justify a machine take-over. Wisdom comes slowly and is different than intelligence.

Still, what about autonomous weapons? Is research into advanced AI in this area beneficial? Are military defense capabilities beneficial? Pro-security? Is the slaughter of robots not better than the slaughter of humans? Could robots be more ethical at “soldiering” than humans? As attorney Matt Scherer has noted, who is the editor of a good blog, LawAndAI.com and a Future of Life Institute member:

Autonomous weapons are going to inherently be capable of reacting on time scales that are shorter than humans’ time scales in which they can react. I can easily imagine it reaching the point very quickly where the only way that you can counteract an attack by an autonomous weapon is with another autonomous weapon. Eventually, having humans involved in the military conflict will be the equivalent of bringing bows and arrows to a battle in World War II.

At that point, you start to wonder where human decision makers can enter into the military decision making process. Right now there’s very clear, well-established laws in place about who is responsible for specific military decisions, under what circumstances a soldier is held accountable, under what circumstances their commander is held accountable, on what circumstances the nation is held accountable. That’s going to become much blurrier when the decisions are not being made by human soldiers, but rather by autonomous systems. It’s going to become even more complicated as machine learning technology is incorporated into these systems, where they learn from their observations and experiences in the field on the best way to react to different military situations.

Podcast: Law and Ethics of Artificial Intelligence (Future of Life, 3/31/17).

The question of beneficial or not can become very complicated, fast. Like it or not, military research into killer robots is already well underway, in both the public and private sector. Kalashnikov Will Make an A.I.-Powered Killer Robot: What could possibly go wrong? (Popular Mechanics, 7/19/17); Congress told to brace for ‘robotic soldiers’ (The Hill, 3/1/17); US military reveals it hopes to use artificial intelligence to create cybersoldiers and even help fly its F-35 fighter jet – but admits it is ALREADY playing catch up (Daily Mail, 12/15/15) (a little dated, and sensationalistic article perhaps, but easy read with several videos).

AI weapons are a fact, but they should still be regulated, in the same way that we have regulated nuclear weapons since WWII. Tom Simonite, AI Could Revolutionize War as Much as Nukes (Wired, 7/19/17); Autonomous Weapons: an Open Letter from AI & Robotics Researchers.

Principle Two: Research Funding

The second principle of Funding is more than an enforcement mechanism for the first, that you should only fund beneficial AI. It is also a recognition that ethical work requires funding too. This should be every lawyer’s favorite AI ethics principle. Investments in AI should be accompanied by funding for research on ensuring its beneficial use, including thorny questions in computer science, economics, law, ethics, and social studies. The principle then adds a list of five bullet-point examples.

How can we make future AI systems highly robust, so that they do what we want without malfunctioning or getting hacked. The goal of avoiding the creation of AI systems that can be hacked, easily or not, is a good one. If a hostile power can take over and misuse an AI for evil end, then the built-in beneficence may be irrelevant. The example of a driverless car come to mind that could be hacked and crashed as a perverse joy-ride, kidnapping or terrorist act.

The economic issues raised by the second example are very important: How can we grow our prosperity through automation while maintaining people’s resources and purpose? We do not want a system that only benefits the top one percent, or top ten percent, or whatever. It needs to benefit everyone, or at least try to. Also see Asilomar Principle Fifteen: Shared Prosperity: The economic prosperity created by AI should be shared broadly, to benefit all of humanity.

Yoshua Bengio, Professor of Computer Science at the University of Montreal, had this important comment to make on the Asilomar principles during an interview at the end of the conference:

I’m a very progressive person so I feel very strongly that dignity and justice mean wealth is redistributed. And I’m really concerned about AI worsening the effects and concentration of power and wealth that we’ve seen in the last 30 years. So this is pretty important for me.

I consider that one of the greatest dangers is that people either deal with AI in an irresponsible way or maliciously – I mean for their personal gain. And by having a more egalitarian society, throughout the world, I think we can reduce those dangers. In a society where there’s a lot of violence, a lot of inequality, the risk of misusing AI or having people use it irresponsibly in general is much greater. Making AI beneficial for all is very central to the safety question.

Most everyone at the Asilomar Conference agreed with that sentiment, but I do not yet see a strong consensus in AI businesses. Time will tell if profit motives and greed will at least be constrained by enlightened self-interest. Hopefully capitalist leaders will have the wisdom to share the great wealth with all of society that AI is likley to create.

How can we update our legal systems to be more fair and efficient, to keep pace with AI, and to manage the risks associated with AI? The legal example is also a good one, with the primary tension we see so far between fair versus efficient. Just policing high crime areas might well be efficient, at least for reducing some type of crime, but would it be fair? Do we want to embed racial profiling into our AI? Neighborhood slumlord profiling? Religious, ethic profiling? No. Existing law prohibits that and for good reason. Still, predictive policing is already a fact of life in many cities and we need to be sure it has proper legal, ethical regulation.

We have seen the tension between “speedy” and “inexpensive” on the one hand, and “just” on the other in Rule One of the Federal Rules of Civil Procedure and e-discovery. When applied using active machine learning a technical solution was attained to these competing goals. The predictive coding methods we developed allowed for both precision (“speedy” and “inexpensive”) and recall (“just”). Hopefully this success can be replicated in other areas of the law where machine learning is under proportional control by experienced human experts.

The final example given is much more troubling: What set of values should AI be aligned with, and what legal and ethical status should it have? Whose values? Who is to say what is right and wrong? This is easy in a dictatorship, or a uniform, monochrome culture (sea of white dudes), but it is very challenging in a diverse democracy. This may be the greatest research funding challenge of all.

Principle Three: Science-Policy Link

This principle is fairly straightforward, but will in practice require a great deal of time and effort to be done right. A constructive and healthy exchange between AI researchers and policy-makers is necessarily a two-way street. It first of all assumes that policy-makers, which in most countries includes government regulators, not just industry, have a valid place at the table. It assumes some form of government regulation. That is anathema to some in the business community who assume (falsely in our opinion) that all government is inherently bad and essentially has nothing to contribute. The countervailing view of overzealous government controllers who just want to jump in, uninformed, and legislate, is also discouraged by this principle. We are talking about a healthy exchange.

It does not take an AI to know this kind of give and take and information sharing will involve countless meetings. It will also require a positive healthy attitude between the two groups. If it gets bogged down into an adversary relationship, you can multiply the cost of compliance (and number of meetings) by two or three. If it goes to litigation, we lawyers will smile in our tears, but no one else will. So researchers, you are better off not going there. A constructive and healthy exchange is the way to go.

Principle Four: Research Culture

The need for a good culture applies in spades to the research community itself. The Fourth Principal states: A culture of cooperation, trust, and transparency should be fostered among researchers and developers of AI. This favors the open source code movement for AI, but runs counter to the trade-secret  business models of many corporations. See Eg.:OpenAI.com, Deep Mind Open Source; Liam , ‘One machine learning model to rule them all’: Google open-sources tools for simpler AI (ZDNet, 6/20/17).

This tension is likley to increase as multiple parties get close to a big breakthrough. The successful efforts for open source now, before superintelligence seems imminent, may help keep the research culture positive. Time will tell, but if not there could be trouble all around and the promise of full employment for litigation attorneys.

Principle Five: Race Avoidance

The Fifth Principle is a tough one, but very important: Teams developing AI systems should actively cooperate to avoid corner-cutting on safety standards. Moving fast and breaking things may be the mantra of Silicon Valley, but the impact of bad AI could be catastrophic. Bold is one thing, but reckless is quite another. In this area of research there may not be leisure for constant improvements to make things right. HackerWay.org.
Not only will there be legal consequences, mass liability, for any group that screws up, but the PR blow alone from a bad AI mistake could destroy most companies. Loss of trust may never be regained by a wary public, even if Congress and Trial Lawyers do not overreact. Sure, move fast, but not too fast where you become unsafe. Striking the right balance is going to require an acute technical, ethical sensitivity. Keep it safe.

Last Word

AI ethics is hard work, but well worth the effort. The risks and rewards are very high. The place to start this work is to talk about the fundamental principles and try to reach consensus. Everyone involved in this work is driven by a common understanding of the power of the technology, especially artificial intelligence. We all see the great changes on the horizon and share a common vision of a better tomorrow.

During an interview at the end of the Asilomar conference, Dan Weld, Professor of Computer Science, University of Washington, provided a good summary of this common vision:

In the near term I see greater prosperity and reduced mortality due to things like highway accidents and medical errors, where there’s a huge loss of life today.

In the longer term, I’m excited to create machines that can do the work that is dangerous or that people don’t find fulfilling. This should lower the costs of all services and let people be happier… by doing the things that humans do best – most of which involve social and interpersonal interaction. By automating rote work, people can focus on creative and community-oriented activities. Artificial Intelligence and robotics should provide enough prosperity for everyone to live comfortably – as long as we find a way to distribute the resulting wealth equitably.


Six Sets of Draft Principles Are Now Listed at AI-Ethics.com

October 8, 2017

Arguably the most important information resource of AI-Ethics.com is the page with the collection of Draft Principles underway by other AI Ethics groups around the world. We added a new one that came to our attention this week from an ABA article, A ‘principled’ artificial intelligence could improve justice (ABA Legal Rebels, October 3, 2017). They listed six proposed principles from the talented Nicolas Economou, the CEO of electronic discovery search company, H5.

Although Nicolas Economou is an e-discovery search pioneer and past Sedona participant, I do not know him. I was, of course, familiar with H5’s work as one of the early TREC Legal Track pioneers, but I had no idea Economou was also involved with AI ethics. Interestingly, I recently learned that another legal search expert, Maura Grossman, whom I do know quite well, is also interested in AI ethics. She is even teaching a course on AI ethics at Waterloo. All three of us seem to have independently heard the Siren’s song.

With the addition of Economou’s draft Principles we now have six different sets of AI Ethics principles listed. Economou’s new list is added at the end of the page and reproduced below. It presents a decidedly e-discovery view with which all readers here are familiar.

Nicolas Economou, like many of us, is an alumni of The Sedona Conference. His sixth principle is based on what he calls thoughtful, inclusive dialogue with civil society. Sedona was the first legal group to try to incorporate the principles of dialogue into continuing legal education programs. That is what first attracted me to The Sedona Conference. AI-Ethics.com intends to incorporate dialogue principles in conferences that it will sponsor in the future. This is explained in the Mission Statement page of AI-Ethics.com.

The mission of AI-Ethics.com is threefold:

  1. Foster dialogue between the conflicting camps in the current AI ethics debate.
  2. Help articulate basic regulatory principles for government and industry groups.
  3. Inspire and educate everyone on the importance of artificial intelligence.

First Mission: Foster Dialogue Between Opposing Camps

The first, threshold mission of AI-Ethics.com is to go beyond argumentative debates, formal and informal, and move to dialogue between the competing camps. See eg. Bohm Dialogue, Martin Buber and The Sedona Conference. Then, once this conflict is resolved, we will all be in a much better position to attain the other two goals. We need experienced mediators, dialogue specialists and judges to help us with that first goal. Although we already have many lined up, we could always use more.

We hope to use skills in both dialogue and mediation to transcend the polarized bickering that now tends to dominate AI ethics discussions. See eg. AI Ethics Debate. We need to move from debate to dialogue, and we need to do so fast.

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Here is the new segment we added to the Draft Principles page.

6. Nicolas Economou

The latest attempt at articulating AI Ethics principles comes from Nicolas Economou, the CEO of electronic discovery search company, H5. Nicolas has a lot of experience with legal search using AI, as do several of us at AI-Ethics.com. In addition to his work with legal search and H5, Nicholas is involved in several AI ethics groups, including the AI Initiative of the Future Society at Harvard Kennedy School and the Law Committee of the IEEE’s Global Initiative for Ethical Considerations in AI.

Nicolas Economou has obviously been thinking about AI ethics for some time. He provides a solid scientific, legal perspective based on his many years of supporting lawyers and law firms with advanced legal search. Economou has developed six principles as reported in an ABA Legal Rebels article dated October 3, 2017, A ‘principled’ artificial intelligence could improve justice. (Some of the explanations have been edited out as indicated below. Readers are encouraged to consult the full article.) As you can see the explanations given here were written for consumption by lawyers and pertain to e-discovery. They show the application of the principles in legal search. See eg TARcourse.com. The principles have obvious applications in all aspects of society, not just the Law and predictive coding, so their value goes beyond the legal applications here mentioned.

Principle 1: AI should advance the well-being of humanity, its societies, and its natural environment. The pursuit of well-being may seem a self-evident aspiration, but it is a foundational principle of particular importance given the growing prevalence, power and risks of misuse of AI and hybrid intelligence systems. In rendering the central fact-finding mission of the legal process more effective and efficient, expertly designed and executed hybrid intelligence processes can reduce errors in the determination of guilt or innocence, accelerate the resolution of disputes, and provide access to justice to parties who would otherwise lack the financial wherewithal.

Principle 2: AI should be transparent. Transparency is the ability to trace cause and effect in the decision-making pathways of algorithms and, in hybrid intelligence systems, of their operators. In discovery, for example, this may extend to the choices made in the selection of data used to train predictive coding software, of the choice of experts retained to design and execute the automated review process, or of the quality-assurance protocols utilized to affirm accuracy. …

Principle 3: Manufacturers and operators of AI should be accountable. Accountability means the ability to assign responsibility for the effects caused by AI or its operators. Courts have the ability to take corrective action or to sanction parties that deliberately use AI in a way that defeats, or places at risk, the fact-finding mission it is supposed to serve.

Principle 4: AI’s effectiveness should be measurable in the real-world applications for which it is intended. Measurability means the ability for both expert users and the ordinary citizen to gauge concretely whether AI or hybrid intelligence systems are meeting their objectives. …

Principle 5: Operators of AI systems should have appropriate competencies. None of us will get hurt if Netflix’s algorithm recommends the wrong dramedy on a Saturday evening. But when our health, our rights, our lives or our liberty depend on hybrid intelligence, such systems should be designed, executed and measured by professionals with the requisite expertise. …

Principle 6: The norms of delegation of decisions to AI systems should be codified through thoughtful, inclusive dialogue with civil society. …  The societal dialogue relating to the use of AI in electronic discovery would benefit from being even more inclusive, with more forums seeking the active participation of political scientists, sociologists, philosophers and representative groups of ordinary citizens. Even so, the realm of electronic discovery sets a hopeful example of how an inclusive dialogue can lead to broad consensus in ensuring the beneficial use of AI systems in a vital societal function.

Nicolas Economou believes, as we do, that an interdisciplinary approach, which has been employed successfully in e-discovery, is also the way to go for AI ethics. Note his use of the word “dialogue” and mention in the article of The Sedona Conference, which pioneered the use of this technique in legal education. We also believe in the power of dialogue and have seen it in action in multiple fields. See eg. the work of physicist, David Bohm and philosopher, Martin Buber. That is one reason that we propose the use of dialogue in future conferences on AI ethics. See the AI-Ethics.com Mission Statement.

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