CoBot Systems

McKinsey identified 63 generative AI use cases spanning 16 business functions that could deliver total value in the range of $2.6 trillion to $4.4 trillion in economic benefits annually when applied across industries

The economic potential of generative AI: The next productivity frontier Generative AI’s impact on productivity could add trillions of dollars in value to the global economy—and the era is just beginning.

The strategic intent to make AI so cheap and easy that everyone can use it runs up against the desire to add safeguards. "If only wealthy hospitals can take advantage of AI systems, the benefits of these technologies will not be equitably distributed." vs five core protections i the US AI policy:
1. Safe and Effective Systems: You should be protected from unsafe or ineffective systems.
2. Algorithmic Discrimination Protections: You should not face discrimination by algorithms and systems should be used and designed in an equitable way.
3. Data Privacy: You should be protected from abusive data practices via built-in protections and you should have agency over how data about you is used.
4. Notice and Explanation: You should know that an automated system is being used and understand how and why it contributes to outcomes that impact you.
5. Alternative Options: You should be able to opt out, where appropriate, and have access to a person who can quickly consider and remedy problems you encounter

Safeguards seem to add costs to AI, which goes against making AI cheap and widespread. If we can convert the problem of safeguards into a problem of effectiveness, we can get to the virtuous cycle where AI becomes cheap and effective while honoring the safeguards.

Each AI owner is already internally incentivized to build an AI-checker to ensure that the AI is effective (i.e., it does what it's supposed to do). That addresses the requirement for effective AI, where unsafe AI is handled as being ineffective. Let's call this sort of AI "Effective AI".

Algorithmic discrimination and data privacy work against the effectiveness imperative of the AI owner. Denial of data would typically be used by the AI as part of its algorithm (what does it mean for my decision that Tom has requested that xyz datapoint should not be used in his case?). Whether by AI or not, discrimination is the basis of decision-making. Locating and combating illegal discrimination can and should rest with the agencies responsible for eliminating it, because those social-good agencies can convert an otherwise open-ended problem to a well-defined set of algorithms. This approach, again, aligns with the natural need for using an effective AI, in this case to further the interests of the social-good agencies. Let's call this AI to be "Social Alignment AI" that its owners will want to be efficient and effective.

Notice and Explanation stems from being fair. As currently worded, it can create an expensive arms race as malicious players use explanations to understand and win against the AIs. A black box that's doubly guarded by Effectiveness and Social Alignment can be sufficient to meet the need for fairness.

Alternative Options deals with the ethics of not trapping people. A real "way out" should enable review and redress, stemming from being just. These are difficult matters anyway, not just for AI, and need to be addressed as a set of "Justice AI" that seems to be an aspect of Alignment AI. The problem is that an unjust AI decision can be trivially easy to find-and-solve or devilishly hard, possibly rife with false-positives and false-negatives. This class of "Social Alignment AI" is likely to use triage approaches.

Separating the concepts of Effectiveness and Social Alignment AI will, I think, provide both cost-effective and safeguarded AI. Each AI owner is incented to make its AI efficient and effective. This ecosystem requires a market-making player to enable each Effectiveness AI to check its outputs against the relevant set of Alignment AIs so that it can constantly locate and eliminate misalignments.

National artificial intelligence R&D strategic plan from the White House | INFORMS Open Forum The White House released the national AI R&D strategic plan and is soliciting public input on critical AI issues. You can weigh in by June 7.FACT SHEET: Biden-H

Can decision-making benefit from an AI driven co-pilot?

The Vice President and CEO of Innovation at Microsoft, Jason Wild, predicted that every job will be transformed by an existence of an AI co-pilot, driven by large language models (LLMs) such as ChatGPT (see https://www.laprensalatina.com/microsoft-every-job-will-have-an-artificial-intelligence-copilot/).

At CoBot Systems, we have been building OR-DA algorithms for decision support for years. For us, these are "co-pilots". They are used for decision-making by car dealers in the USA (see https://www.frogdata.com).

So decision-making does benefit from an AI driven co-pilot because it provides decision-makers with the embedded intelligence of an analytics practitioner who has learned by immersion in the business context, and that gets updated as we (the system providers) find ways to improve it.

Using LLM for these business decision agents is non-trivial. Think of it this way:
1. It's a known category of problem, and an optimal solution exists. This would use classical OR-DA.
2. It's a new category or an unsolved problem. When used in a business context, it is stated as developing situation awareness (akin to battlefield awareness) and then path-finding in explore/exploit cycles. Again, classical methods provide firm guides for both awareness (including anomaly detection) and experiment-assessment cycles.

If we include LLMs in the decision-support algorithm, the analytics practitioner is still required to build it. That practitioner can use an LLM as a co-pilot. Analytics practitioners acting as Decision Coaches (h/t Dr. Steve Barrager and Dr. John Milne) would also have a role to play in helping business decision-makers use the decision co-pilots because of the range of concerns to be handled. Those Decision Coaches could use an LLM as a co-pilot. So there are three kinds of LLM-co-pilots here:
1. Decision Co-Pilot = classical OR-DA co-pilots used by business decision-makers, augmented by LLM only where appropriate.
2. Decision Co-Pilot Maker = LLM to help the Decision Co-Pilot builder (an OR-DA practitioner). Such a Maker can democratize the role of analytics practitioners.
3. Decision-Coach Co-Pilot = LLM to guide the Decision Coach (an OR-DA practitioner) who helps business decision-makers use the Decision Co-Pilot.

Are we getting to the point of having an INFORMS BoK LLM that could serve these three uses?

This is in a discussion thread from INFORMS.org (see https://connect.informs.org/discussion/can-decision-making-benefit-from-an-ai-driven-co-pilot).

Can Decision Making benefit from an AI driven co-pilot? | INFORMS Open Forum Friends,The Vice President and CEO of Innovation at Microsoft, Jason Wild, predicted that every job will be transformed by an existence of an AI co-pilot, drive