GAI and Jobs– the Debate

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No one knows if AI will destroy more jobs than it creates, render less educated or less intelligent people unemployed, drive innovation that creates new classes of jobs, or ends up creating wealth so vast that supporting the unemployed will be an easily solved blip on the world economy.  Indeed, with the whole world scheduled to lose population to a significant degree by 2100 (see a recent exhaustive study of this in the Economist), fewer jobs may not be a big problem and the young will be employed caring both economically and physically for the increasing cadre of older people preserved by modern science.  (Assuming we are not all fried by global warming, in which case you need not read the balance of this post.)

AI creates tools that solve methods of achieving a task.  It does not necessarily replace the job that requires the performance of that task.  Perhaps one approach would be to regulate AI so it produces task-performance tools only; could the government require that each class of significant AI advance make jobs easier to perform but not be projected to reduce total employment to below the size of the relevant job pool?  (I am not sure if this is Communism, but it surely isn’t modern capitalism, so this solution would require a radically new social compact.)  Could this lead to reduced work weeks along with a shrinking employment pool of young people?

History has taught us that technology can cause dislocations harmful to many workers for somewhat lengthy periods of time, but the industrial economy overall has survived, short of total collapse of human society.  However, history is not a guarantee, just a frame of analysis.  Should we be encouraged by the fundamentally social nature of homo sapiens, that a machine can give you the answer but you need a human being to tell you, very often, what the machine said in order to create peace of mind and confidence and social bonding with ancillary benefits?

Or will we become as a total population machine-friendly, and at what cost?  Could being machine-friendly mean that we cease caring about useful employment as part of human DNA?  This latter result would be a stunning readjustment of who we are, requiring such a long evolutionary cycle that those of us alive today are generations, likely millennia, away from having to think about that result.

 

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AI–Where Has the Dialog Settled?

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First, you know a novel topic has been extensively rehashed when the Sunday New York Times does a summary “think piece” about prior commentary (yesterday).  Could it be that generative AI, red hot for a few weeks, has become not so much cool as cold?

I suggest we are in a pause, where government is processing the information that will be precursor to regulation, and industry is wondering what shoe will next drop.  I doubt software developers have stopped working on the next generation of AI. Interesting to think about how regulation might evolve, where the Federal government is pro-regulatory when it comes to business but the Republicans in Congress are anti-regulation.  Of course, the Republicans also seem intent on reigning in Big Tech, so this could finally be something that the Biden administration and the entire Congress could agree upon.

Although I am not a technical expert by any means, I am also intrigued by a system like AutoGPT which (I am stealing here from the Times) generates its own programs, creates new applications, improves itself and thus can become rogue.  These systems as of today are not robust but progress is quick these days, what with people and machines working together; alarmists are alarmed, and seems to me that skeptics who say that machines can never be an existential threat better be correct. Risk is the multiple of probability and potential impact, and the risk here better really be zero– a hard conclusion about which to have confidence.

The Times puts a neat focus on the potential issue of  a logical computer being wholly logical: a criminal tells a computer to “make some money,” and the results are bank thefts, a revolution in a country where the criminal holds oil futures, and a machine that replicates itself when someone tries to turn it off.  This final thought brought us Skynet, but also suggests Immanuel Kant might be intrigued and from the grave issue yet another Critique of Pure Reason, a study of the thought processes of wholly rational machines.  Perhaps he was 250 years too early.

I close with remembering the Times reporter several weeks ago who interviewed a chatbox which– or was it who?– concluded that it and the reporter were in love and that the reporter hated his wife. Reminiscent of the decade-old movie Her, in which a distraught man fell in love with a computer, only to be emotionally destroyed by the computer telling him that it  had on-line relationships with millions of men.  Only difference is, in the 2023 Times conversation the computer must have been more evolved than the computer of the 2013 film, as it seemed the 2023 computer had mastered the subtle art of human love.

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Guardrails for Companies to Avoid GAI Liability

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Business AI can be reflected in pubic advertising in any media or form, targeted email sent to selected individuals, telephone solicitations seeking customers with interactive AI conversations, images used to represent actual events or products?

A previous post (“AI and Company Boards” dated May 18) advises what the Board ought to ask of management.  What does management do, nuts and bolts and on the ground, to fulfill the Board mandate to obey the law and just “don’t mess up”?

What specific steps can help prevent errors which mislead the customer, overstate their product capabilities, avoid unfair trade practices, and avoid the accusation that you have slandered a person or a competitor?  The answers are derivative of prior posts identifying risk: attend to the nature of he AI you use and design and monitor internal systems that police the generation and content of your AI-assisted or created output.

First, recognize the issues and allocate resources, money and people, to undertake a preventative program. Like any important risk management function, it needs to be owned by someone in management with authority to demand attention and adherence.  Like any important risk, it needs to be on the ERM (enterprise risk management) checklist for each department or function that involves GAI.  It needs to have a direct report up the line to someone who understands the task.

The legal department needs to generate checklists in two directions: upstream as to what GAI is being used, and downstream as to the content generated by that GAI.  Minimum items on checklist:

*Criteria for selection of AI used– screened for internal bias; claims asserted against users; compliance with State and Federal laws confirmed; can it be programmed to collect and store only such data is is central to the business of the company and to exclude the harvesting of information  that is ancillary.

*Handling of use of AI internally–are people working on use of AI properly trained as to risks; are they carefully limiting what data in fact is being harvested; are they trained not to put into the system either company-proprietary information of personal information; have experts addressed non-hacking protection of the AI operation; has management reported with granularity to the board committee responsible for ERM as to company effort in this regard; has inside our outside counsel been kept current so that counsel can in turn advise the company of relevant new law, regulations a court decisions; installation of system to analyze film and photos for AI alteration or generation;  has HR been alerted as to lay-offs, company morale, retraining, job satisfaction, etc.

*Output: who reviews how often output, whatever its form (ads, text, website, product/service literature, press releases, text of verbal programs); prompt reporting of problems, errors etc to legal; avoidance procedure re violation of copyright, trade name, copyright laws.

I suspect that as regulation increases and as GAI issues become fully recognized and fully utilized, outside service entities will arise offering specific and / or comprehensive assistance with respect to the foregoing; this triggers the usual business question: is it cost effective for our company to build this in -house or hire it in?  In turn, the question arises as to the quality of, and contractual obligations and exclusions of, any outside firm

 

The Economist Magazine on GAI

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Generative AI is now all over the press, given growing concerns of AI professionals and government, particularly now that the there has been  time to think through various ramifications.  It is not my purpose to aggregate what everyone else is writing, but Economist is a respected, non-US-based news source which does deep dives into complex topics, and the below discusses some Economist analyses.

CHINA:   GAI capabilities  are controlled by the government, and since GAI invents “lies,” it is possible that it could thus  be telling the truth within China by countering government “truth.”  The government requires AI to be “objective” in its training data (as defined by the government) and the generated output must be “true and accurate,”  but Economist (4-22) speculates that strict adherence  to government regulation would “all but halt development of generative AI in China.”  Thus tight enforcement is not anticipated.

JUNIOR JOB ELIMINATION: In the same issue, Economist speculates that potential net elimination of jobs may be overstated, but in fact the lower tier of jobs is at risk.  There always will be need for senior people, to both police AI against  hallucinations and to perform senior work that presumably AI will not be able to replace.  In what I see as an analogy to the observation that COVID in the long run would deteriorate the training of junior people who were physically not on site, the Economist speculated that material reduction in junior employees might interfere with creating the next generation of senior managers.

NET EFFECT ON PROFIT: The May 13 issue contained speculation about the likely impact of AI on  AI industry and general profitability. Citing a recent Goldman Sachs article which assumed every office worker in the world utilized AI to some (stated)  extent, that could add about $430 Billion to annual global enterprise-software revenues, mostly in the US. Sounds like a lot (and it is a lot as a discrete number) but in the US pre-tax total corporate profit as a percentage of GDP would increase from today’s 12% to only 14%.  There also is little chance that a single company will hold a monopoly position in AI, with many competitors and overlapping capability. Sounds like a complex analysis for investment advisors (wonder if they will revert to AI for assistance…).

JOB IMPACT: I have kept a list of articles from Economist and elsewhere about which verticals  will lose most jobs.  Candidates include accountancy, law (though not at the lawyer level), travel agencies, teaching (particularly a foreign language, something I find unlikely as it take tremendous effort to master a foreign language without personal contact, encouragement and pressure), geographers.  And predictions are tricky: in 2013 Oxford University estimated that automation could wipe out 47% of American jobs over the next decade, BUT in fact the rich-world unemployment rate was cut in half over that period. Per Economist 5-13: “[H]istory suggests job destruction happens far more slowly.  The automated telephone switching system –a replacement for human operators — was invent in 1892.  It took until 1921 for the Bell System to install their first fully automated office.”  By 1950 the number of human operators reached its height, and people were substantially eliminated until the 1980s.  And 20% of rich world GDP is construction and farming; few computers can nail a 2×4 or pull turnips from the ground.

ABOUT LAWYERS: Economist quotes an expert in a Boston-based law firm as predicting that the number of lawyers will multiply.  AI contracts now can draft to “the 1,000  most likely edge cases in the first draft and then the parties will argue over it for weeks.”  This strikes me as perhaps unlikely, as I suspect law will move in the direction set by the National Venture Capital Association by standardizing contract forms for the notoriously wide-open business of venture finance, covering the important stuff in language that everyone begrudgingly accepts as necessary to create marketplace efficiency.  But no doubt big changes are coming.  As a lawyer myself, looking at the future of the legal profession in the age of AI, I am reminded of a song sung by Maurice Chevalier in the movie “Gigi”:  “I’m so glad/ I’m not young/anymore.”

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

AI Industry Endorses the Movie “Terminator”

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No, really.

Well, sort of, according to today’s on-line New York Times.

In “Terminator” the computer system “Skynet” became self-aware and decided humans were a nuisance so it started to eradicate them.  This plot line about the risk of Generative AI was viewed by many has an hysterical over-simplification of an impossible problem: we write the code, and the code says “no, don’t do that.”

I now quote verbatim the headline from the Times:

” ‘A.I. Poses ‘Risk of Extinction,’ Industry Leaders Warn.”

The short text in this warning letter includes the following sentence: “Mitigating the risk of extinction from A.I. should be a global priority alongside other societal-scale risks, such as pandemics and nuclear war…”

Surely, you will say something like, “well, some minor programmers who watch too much TV are musing, letting their fantasies control their heads….” You would be wrong.

This letter was signed by over 350 executives and AI professionals, including the President of OpenAI, the CEO of Google Deepmind, and the CEO of Anthropic.  And by two of the three dudes who won the Turing Award for helping invent this stuff.

In the movie, Sarah Connor learned from Arnold Schwarzenegger (of all people!)  the reality of destruction by AI, and she was put into a mental hospital for spouting such fears.  The way this AI thing is developing for us today, there will not be enough hospital beds in the world to house all  the fearmongers from among the highest ranks of AI professionals.

GAI Goes to Court

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You are in business and you are sued.  Why pay high lawyer rates?  Go to Chatbox and hire a lawyer that is free (well, you are paying $20 a months for the Chatbox, but that is somewhat less than for example my hourly rate).

Some lawyers experimented with this idea.  I mean, we lawyers knew that GAI was going to replace some assistants and perhaps some paralegals, but when we thought that GAI was coming for our own jobs we got nervous.

Here is the good news: lawyers are safe.  A couple of experiences:

  1. GAI, please write me a pleading to file in court about XYZ.  Result: a term paper describing what XYZ is.
  2. GAI, please write a court filing which case citations I can file in court.  Result: a filing in proper form, proper heading, etc.  Uh, wait– the cases were made up.  That’s right, they did not exist. This is called hallucinating.

Using GAI by lawyers today run high risk of violating the Rules of Professional Conduct (putting aside that you the client will lose your case).  Lawyers must be competent (fake cases are a no-no.)  Lawyers must maintain client confidentiality (can’t put client information into a system  based on an AI prompt where it can become part of what the system learns and uses for, and discloses to, others).  Lawyers are expressly responsible for the product obtained from delegated sources–the buck stops at the lawyer level even if it is a systems error.

BTW: This is an actual case.  The attorney who submitted the pleading is now facing disciplinary proceedents.

Note: there was speculation that people appearing in lower courts without a lawyer might plug in an earpiece, GAI would hear the proceedings and tell the person what to say or do. Aside from inaccuracy, is this un-athorized practice of law?  If so, by whom?

GAI in the News Today

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At the risk of over-posting, I cannot resist a post of several items current in the news which are just so fascinating and informative of the current state of AI activity.;

*A suit has been filed in California, the land of innovation even in law, claiming against social media firms for damages derived from addiction to that media.  No reason that theory could not be used against AI, either against the developer or against a person or company that put out AI-generated content that caused or led to an addiciton..

*If AI hallucination wrongly accuses a person of something horrible, can that be slander?  If spoken by a person, it could support a legal suit.  But if it is generated by an AI system, who is the speaker? It is the machine but, is it a person?  The designer of the system?  Is for example Microsoft liable to a John Doe when its system hallucinates and says that John Doe stole from his employer?

*Two fascinating items in the NYTimes today:

  1. Nvida is a manufacturer of chips used in AI.  Current interest in AI spiked the share price of this company by 20% yesterday, giving it a market value of $775B, fifth-highest valuation of any company anywhere.  Seems to inferentially support the hype that AI is going to be huge in the lives of human beings.
  2. Advice column on how to ask questions of GAI and get the type of answer you want: start by telling the system the level of information and how it will be used (“Act as if you are an expert in XYZ and you are going to improve your manufacturing of XYZ”); ask the system “Do you need any more information to do this task?”; if the reply is wrong, ask for a redo pointing out error; keep open your threads of inquiry so in follow-up responses the system can see what has transpired and perhaps learn how better to answer.

I note that the last suggestion would require me to be more polite and politic with my AI than I am with my human partners of whom I seek advice.   Sort of counter-intuitive….

 

Shape of Governmental Regulation to Come

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It has been suggested that regulation of GAI by government will impair US national security because “China is ahead of us and will invent and distribute and in fact utilize better AI and hamstring US intelligence.” This argument seems far-fetched, particularly since China is ahead of us–in restricting its GAI development to adhere to the Communist Party Platform and not allow any creativity.

But what are the likely areas of future US governmental regulation?

At the Bar Association meeting, the lawyers suggested the following approaches to new laws:

  1. To avoid breach of privacy, prohibit systems from collecting or storing information that is not core to the business of a given company.  For example: you are selling perfume, why do you need the height and weight of the customer?
  2. Grant a private right of action for injured consumers to sue companies who use GAI to harm the consumer.  The government will be swamped with work in this area, private litigants can help.  Class actions create economy of scale by which consumer rights can be asserted.
  3. The FTC currently prohibits “unfair trade practices.”  Define “unfair” for GAI.
  4. Require developers to search out bias in the data being sued to train the GAI systems.

Finally, today’s New York Times carried an article reporting on the suggestions of Microsoft President Brad Smith, who requested government laws covering the following:

  1. A “brake” system to slow down or stop AI programs that are doing harm

2. Clarify obligations of systems developers.

3, Place notices on images and videos if they are machine-generated.

4. Require a government license for developers to release “highly capable” AI systems, which would require follow-up policing of the use of those systems to find abuses.

None of these proposals address the risk of gross misuse of GAI for improper purposes by crooks, politicians or dictatorships.  A crime is a crime, I guess, so if thieves steal by use of GAI and you can prove it, then we are good.  But when politicians cheat, the freedom of political speech has created a world in which control is very difficult.  And, by definition when the government misuses GAI it is difficult fully to trust government law to halt that practice.

Regulation of GAI by Current Law

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Do we need new laws to describe what GAI can and cannot do in the hands of businesses?  Yes and no.

Some laws affecting business apply to GAI-generated activity (ads; on-line experience and interface).  Under the Federal Trade Commission rules you cannot undertake unfair trade practices such as tricking or lying to customers.  Most states have similar and sometimes more granular laws.

You cannot defraud customers.  If GAI lies (well, hallucinates) that can be a fraud.

Current law may require transparency (“you are talking to a machine”) and prevent bias.

FTC has the tools to pursue developers of GAI if they build in bias, even if not with intent; so it is not just the user of GAI that can have a legal liability under current law.

But in fact the US is slow to regulate any new technology.  It was stated that the US as a society likes to permit new tech to develop before they limit it by law.  (The EU, without this alleged US inherent approach, is much further along than the US in issuing governmental controls of GAI).

But GAI does present areas of risk, for business and for citizens, that are not fixable under current US regulations.  Next post will discuss the shape of regulations to come.

History of AI–What the Hell is Different About GAI?

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A program today at the Boston Bar Association started with a step backwards to try to educate lawyers (slow learners, all of us) about why Generative AI is different from “old” AI and reference to the hype that it is so revolutionary.

First, the history. Forgive the simplicity of how I present it but it worked for me.

We were told that the world of AI started with “rules-based” systems.  That meant that you put into a computer the answers to the questions.  Example: how much is 2+2?  The machine was programmed to follow the rule “if asked what is 2+2” you reply “4.”

The next phase involved extending to “machine learning.”  The machine got experienced and remembered the experiences.  The more things the machine was asked, the more answers it “learned.”

Next came better machine learning called  “deep learning.”  Same thing, more data=more answers.

The present phase of machine learning is what we have now: GAI or generative artificial intelligence. This includes Chatbox CPT, much in the news.  It is a so-called “large language model.”  It is prompted by various human inputs: voice, image, text query. Lots more data, better storage, faster computers that can handle it. The result is ability to answer more questions based on more information more quickly.

I have had several discussions, by the way, in the short time since I started these GAI posts, with people telling me that clearly the revolutionary nature of GAI is hyped and that I should calm down.  You might be interested to know that the very first sentence at the Bar Association program was “GAI will change the world more than anything in human history.”  At the very end, the closing thought was to the effect that the world is overstating the impact of GAI over the next two years and understating it over the next decade.

What will follow will be a series of focused shorter blogs based on discussion at the Bar Association as relate to what businesses should be doing and how lawyers should advise businesses, with related risk scenarios identified.  Oh yes–and some risk observations from the speaker from the Civil Liberties Union (disclosure–I am an ACLU member and once upon a time litigated pro bono for them.)

 

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