No One Has Been Paying Attention For A While Now: What Recent Experiences With Remote Juries Tell Us About Our Distracted World

In Court, Everett Collection

Some California courts are holding jury trials during the coronavirus pandemic. Logistics have been a difficult challenge. But the biggest problem — one far more consequential than any technical issue and more pervasive than what happens in legal proceedings — is many people’s inability to pay attention anymore.

In one case, a juror left his computer to attend to food on the stove. Another juror could be seen lying in bed. Jurors switched focus away from court proceedings to other screens, kids, pets, and whatever else was happening at home.

In other words, jurors did what everyone does during video meetings. Juror distraction is merely a special case of a general problem.

Here’s what we can do about it.

(Link to full essay, originally published by The Daily Journal, September 11, 2020)

State Bar Opinions On Safeguarding Client Data

Here are links to State bar opinions on safeguarding client data.

Maine

Opinion #207. The Ethics of Cloud Computing and Storage

Massachusetts

Opinion 12-03. Cloud storage

New Hampshire

2012-13/04 The Use of Cloud Computing in the Practice of Law

New York

Opinion 842 (9/10/10)

Oregon

Opinion No. 2011-188 - Information Relating to the Representation of a Client: Third-Party Electronic Storage of Client Materials

Pennsylvania

Formal Opinion 2011-200 - ETHICAL OBLIGATIONS FOR ATTORNEYS USING CLOUD COMPUTING/ SOFTWARE AS A SERVICE WHILE FULFILLING THE DUTIES OF CONFIDENTIALITY AND PRESERVATION OF CLIENT PROPERTY

Washington State

Advisory Opinion: 2215 - Online data storagage managed by third party vendors

California

2017-174: Electronic Client Files

2010-179: Confidentiality and Technology

2012-184: Virtual Law Office

Data Breach Notification Laws

50 states, the District of Columbia, Guam, Puerto Rico and the Virgin Islands have laws requiring entities to notify individuals of security breaches involving personally identifiable information.

The National Conference of State Legislatures assembled this very handy list.

How Legal Professionals Must Lead In The Age Of Machines

My newest article is now up at ABA's Law Technology Today

AI Falsely Accused Thousands Of Fraud?

Civil rights attorney Jennifer L. Lord represents clients in wrongful termination cases. In 2013, she noticed a common story emerging:

“We were experiencing this rush of people calling us and saying they were told they committed fraud,” Lord said. “We spoke to some of our colleagues who also practice civil rights and employment law and everyone was experiencing this.”

Lord and her team then discovered the Michigan Unemployment Insurance Agency in 2013 purchased the algorithmic decision-making computer called MIDAS, and when the agency did so, it also laid off its fraud detection unit.

“The computer was left to operate by itself,” Lord said. “The Michigan auditor general issued two different reports a year later and found the computer was wrong 93 percent of the time.”

The government accused about 40,000 people of fraud and seized close to $100 million by garnishing their wages or seizing tax refunds. This led Lord and her team to file the class action lawsuit Bauserman v. Unemployment Insurance Agency (subscription required), and the Michigan Supreme Court recently ruled the case may proceed.

According to Lord, it took some digging just to learn that the decision was performed by an algorithm. Recognizing algorithmic — as opposed to human — decisionmaking, has become an increasingly important skill, one of Yuval Noah Hariri’s 21 Lessons for the 21st Century.

This also raises the question of whether machine can and should provide due process. In this case, fraud requires proving intent, involving the subtle and inferential determination of what people were thinking under the circumstances. Are algorithms currently up to the task? How soon before they are?

It also raises the question of how algorithms should be used to make law enforcement decisions more generally. With facial recognition and surveillance data increasingly available, using algorithms to make decisions becomes more attractive. Just ask Chinese authorities.

Finally, this reminds me of the situation in Baltimore, where a cash-strapped municipality finds itself overwhelmed with technological advancement. For Baltimore, it was the inability to fight back against malware. Here, the state may have been tempted by what seemed like a good technological substitute for an expensive government function.

For now at least, significant government decisions still need to be made by humans. As algorithms are incorporated into government decisions, the process must be disclosed and made as transparent as possible. And critically, there must be a way to appeal the decision to a human.

New Efforts At Autonomous Vehicle Legislation

In 2017, the U.S. House of Representatives passed the SELF DRIVE Act governing autonomous vehicles, but it stalled in the Senate. Last year, lawmakers failed to pass a bill before the December recess.

Today, the House Energy and Commerce Committee and the Senate Commerce, Science, and Transportation Committee announced work on a “bipartisan and bicameral basis to develop a self-driving car bill.” They request input from automakers, safety groups, and other stakeholders before August 23. David Shepardson, reporting for Reuters:

“Right now various countries are exploring regulations that will shape the future of autonomous vehicles, and the U.S. risks losing its leadership in this life-saving, life-changing technology, so we urge Congress to move forward now, this year,” spokeswoman [for the Alliance of Automobile Manufacturers] Gloria Bergquist said.

Since the new bill is being written with input from both chambers, this version stands a better chance of avoiding a breakdown like last year.

On Email Disclaimers

On whether to create email disclaimers that have:

no qualms about indulging in the more obnoxious trademarks of legalese, including but not limited to (i) the phrase “including but not limited to”, (ii) the use of “said” as an adjective, (iii) re-naming conventions that have little to no basis in vernacular English and, regardless, never actually recur (hereinafter referred to as “the 1980 Atlanta Falcons”), (iv) redundant, tedious, and superfluous repetition of synonymous terms . . .

The whole thing is hilarious and spot on.  I think I'm going to remove said disclaimer right now.

(Via Ben Brooks)

John Henry Reviews Documents

Integreon has an interesting discussion on a recent study pitting humans against machines.  No this isn't about supercomputers and Jeopardy! It's something much practical:

The underlying study by a trio of recognized experts in cognitive science, information management, and e-discovery, Herb Roitblat, Anne Kershaw, and Patrick Oot, is described in detail in their journal article, Document Categorization in Legal Electronic Discovery: Computer Classification vs. Manual Review, published in the January 2010 issue of theJournal of the American Society for Information Science and Technology [link available at the Posse List].

It raises - and partially answers - the important question whether we are approaching a breakthrough in terms of the capability of automated review tools to render ‘consistent’ and ‘correct’ decisions, as measured against an existing standard, while classifying documents in a legal discovery context. The study pitted two teams of contract attorneys against two commercial electronic discovery applications to review a limited set - 5,000 documents - culled from a collection of 1.6 million documents. The larger collection had been reviewed two years earlier by attorney teams in connection with a Second Request relating to Verizon’s acquisition of MCI. The authors’ hypothesis was that “the rate of agreement between two independent [teams of] reviewers of the same documents will be equal to or less than the agreement between a computer-aided system and the original review.”

The study set out to test whether an automated review tool would show similar levels of agreement with classifications made by the original reviewers as did the two contract teams. The two re-review teams agreed with the original review on about 75% of document classification decisions; the commercial automated applications fared slightly better.

There a number of obvious (and not so obvious) flaws in the study, which the Integreon post nicely lays out. My first reaction is that "rate of agreement" is a lousy benchmark, since the measure conflates too many significant variables.

I'm also fascinated by this quest for the document review holy grail: total automation. Contrary to lean principles, these managers seek to automate the process without fully understanding how it works manually. Just exactly how and why do review document reviewers make different calls?

And what about a hybrid approach?

A potential hybrid model would have senior attorneys review representative sets of documents and the tool analyze features of the reviewed documents to identify and auto-tag “like” documents in the larger collection. As the review proceeded, the tool would ‘percolate’ to the review team’s attention subsets of documents from the collection dissimilar from those already reviewed. Based on the reviewers’ decisions as to these documents, the tool continues to apply tags to more of the collection.

The attraction of this approach is two-fold: human attorneys are still making initial determinations but the application magnifies the effect of their determinations by propagating decisions to similar documents throughout the larger collection. It has been suggested that, in the proper context, this approach would permit a single attorney to “review” a vast collection of documents in several hours. A test of that claim is warranted and, if the premise were proved, it would be impressive and could directly influence the increased use of automation in review, even if, for all the reasons stated above, wide adoption of such processes would take a while.

As a lawyer who likes to tightly control processes, I'll admit the attraction of this approach. As one moves down the hierarchy in any litigation team, deep knowledge of the client and issues is inevitable lost. If technology can leverage the knowledge of the most engaged, the better the result, theoretically.

(cross-posted at California E-Discovery Law)

Does Automation Diminish Our Basic Skills?

Photo Credit: Rui Caldeira

Pilot Patrick Smith has another interesting article on cockpit automation and flight safety, something this blog has considered before.

Has automation reduced pilots' basic "stick and rudder" skills?  His answer:  "Probably, yes."

But the more interesting discussion is how automation has grafted a new technological skill set onto basic flying:

[A]utomation is merely a tool. You still need to tell the airplane what to do, when to do it, and how to do it. There are, for example, no fewer than six different ways that I can program in a simple climb or descent on my 757, depending on preference or circumstances. The automation is not flying the plane. The pilots are flying the plane through this automation.

A fitting metaphor for other knowledge work.  Technology hasn't changed what we do, as much as changed how we interface with machines to get it done.  The tools have changed.  The work, fundamentally, has not.

Of course, interfaces are complicated and can even add to our overall workload:

If you ask me, the modern cockpit hasn't sapped away a pilot's skills so much as overloaded and overburdened them, in rare instances leading to a dangerous loss of situational awareness.

A danger for all of us.  Alarms, notifications, badges, and our ever-expanding landscape of electronic inputs, distract us from real work.  Whether that's landing a plane, or delivering a project.

This has given birth to a meta-skill: the ability to sift, filter, and organize the elements of our work.  Our first challenge, then, is to maintain situational awareness in a complicated world.

Update:  Interesting post on maintaining situational awareness in e-discovery.

10 Things To Check Before Every Presentation

There's a lot to remember when giving multimedia presentations. PowerPoint or KeyNote presentations involve the complex interaction of your computer, your software, the presentation file, a display screen or projector, your remote, and the audio system. And increasingly, presentations are given as webinars, where the presenter (i.e. you) may be in charge of an even greater scope of technical requirements, including gadgets in your office that can interfere with your presentation. Not to mention that co-worker who barges into your office without knocking. A lot can go wrong. So how can you minimize the risk of technical problems? Don't spend valuable mental RAM thinking about the little but important things you might forget. Here's a simple checklist:

1. Screen saver....................................Disabled

2. Power settings.................................Never turn off (all modes)

3. Multiple displays (for webinars)........Disconnected

4. Email notifications...........................Turned off

5. Other popups and notifications..........Disabled

6. Ringers (cell phone, office phone)......Turned off/DND

7. Sign on door (for webinars)...............Displayed

8. Glass of water...................................Filled

9. Outline............................................On podium/desk

10. Presentation.....................................Open/slides loaded

This is a work in progress and I welcome your comments. And for any particular presentation, there may be more to add to your list. Also consider having multiple backups of your presentation ready to go, as detailed in this excellent post.

Now go knock 'em dead.