Business, Legal, and Ethical Aspects of Artificial

1Business, Legal, and Ethical Aspects

of AI in Information Governance© 2019October 17, 2019

ARMA Boise Valley Chapter – October 17, 2019

Business, Legal, and Ethical Aspects of Artificial Intelligence

in Information Governance





Defining “Artificial Intelligence”

Machinelearning

nting layers of compounding non-linear combinations of variables…



“A number of technologies under the umbrella of artificial intelligence, such as machine learning,

natural language processing, expert systems (the ability to emulate decision-making of a human expert)

and others, that allow computers to perform things that normally require human intelligence.”




Differences in degree (volume and complexity) Differences in kind (“brute force” versus

independent agency)Renders decisions based on statistical correlations,

not “cause and effect”




Machine Learning “Algorithms that parse data, learn from that data, and then

apply what they’ve learned to make informed decisions” Common example: playlist recommendations, based on other

listeners with similar interests “Fine-tunes” itself with outside input

Deep Learning Subset of machine learning Built-in layer of automated evaluation to constantly get better

on it’s own




From “Deep Blue” to “AlphaGO”



Ancient Scrolls of Herculaneum



Practical Applications of AI

The best use cases for AI involve seeing things, especially patterns, that humans

can’t or aren’t good at.




Line Functions Predictive analytics for

sales and marketing Chat Bots for customer

service Knowledge Management

on steroids Contract management

Staff Functions Job applicant screening Auto classification Data remediation Monitoring

communications




Example 1 (no endorsement intended): Pactum® assists with contract negotiation by

analyzing contract clauses and negotiation points from a variety of sources (legacy contracts, drafts, emails, interviews with employees and clients, etc.) much the same way as AI is used to play games

It can get “smarter” over time May sit on top of a contract management

system




Example 2 (again, no endorsement intended): Evidence Optix® creates “heat maps”

that identify relevant data custodians and sources and ranks them by relative accessibility

Development scenario: 40 product liability class actions involving 11 products and 2,000 potential custodians world-wide



IBMs Watson helps Judge





Yes, an Israeli small claims court ruledthis was evidence of a lease contract.




The same ASCII code is rendered very differently by different platforms. AI can accurately interpret it, regardless.



PredPol Designed to predict when and where crimes will take

place, with the goal of reducing human bias in policing Simulation of PredPol’s algorithm to drug offences in

Oakland, California, repeatedly sent officers to neighborhoods with a high proportion of people from racial minorities, regardless of the true crime rate in those areas

Epic AI Fails in the Law



COMPAS Algorithm used for bail and sentencing decisions Black defendants were twice as likely to be incorrectly

labeled as higher risk than white defendants 60 % rate of accuracy in COMPAS scores was the same

for black and white defendants, so developers claimed that a test correct in equal proportions for all groups could not be biased

Epic AI Fails in the Law



“There is not a single instance of AI that truly replicates, let alone beats, human intelligence.” Self-driving cars: not really there yet Narrow AI v. Artificial General Intelligence (AGI)

“AI is not free from bias; the danger is that it can automate bias.” In a Google Images search for “CEO,” just 11 per cent of

the people it displayed were women, even though 27 per cent of the chief executives in the US are female

Critiques of AI



AI development relies on large data sets, raising privacy and security concerns “Data lakes” typically fall outside of IG

AI, and in particular “deep learning,” cannot be explained in human terms “Transparency” is not a realistic solution

Critiques of AI



What “Transparency” in AI Looks Like



The more advanced the AI, and therefore the more accurate it is, the harder it is to explain.

(But when someone asks you to “explain yourself” do you explain how the neurons in your brain are

triggered?)

The Transparency Tradeoff



Direct v. indirect explainability, or procedural v. substantive explainability Can a human understand the machine’s “reasoning” Can a human understand the machine’s decision,

regardless of the “reasoning” behind it?

“Explainability”



The appropriate level of explanation depends on several factorsWho does the decision affect? How severely does it impact them?What recourse to they have to challenge the decision? Is liability for a poor decision judged on a “strict” or

“fault” basis?

“Explainability”



Laws Affecting the Implementation of AI

General Data Protection Regulation (GDPR) GDPR Article 22 states in relevant part that individuals “have the right not

to be subject to a decision based solely on automated processing.” Even if a legally binding right to explanation is embedded in the text of the

GDPR itself, the right may only apply in limited circumstances (e.g., when a negative decision is solely automated and has legal or other similar significant effects)

The right to an explanation essentially means that users can demand data underlying algorithmic decisions made about them, including in recommendation systems, credit and insurance risk systems, advertising programs, and social networks




California Consumer Privacy Act (CCPA) of 2018Will take effect on January 1, 2020, with enforceability

to begin July 1, 2020, or six months after publication of the implementing regulations, whichever comes first

Certain provisions require organizations to provide consumers with information regarding the processing of their data during the preceding 12-month period




Various laws restrict or prohibit the use of certain categories of information, e.g.: Title VII of the Civil Rights Act of 1964 The Equal Credit Opportunity Act (“ECOA”) The Fair Credit Reporting Act The Americans with Disabilities Act The Age Discrimination in Employment Act (“ADEA”)




Various laws restrict or prohibit the use of certain categories of information, e.g.: Fair Housing Act (“FHA”) Genetic Information Nondiscrimination Act (“GINA”) Health Information Portability and Accountability Act

(HIPAA)




Various laws restrict or prohibit the use of certain categories of information, e.g.: 4th Amendment (for government actors) Fair Trade and Antitrust laws, e.g.: Search rankings that promote their own products Paid prioritization on search results Social media algorithms trained to promote particular points

of view




New laws are being proposed every day August 17, 2018: “Proposed Private Right of Action by

New York City’s Automated Decision Systems Task Force” Initiative seeks “a law providing a private right of

action for individuals or groups of individuals that are injured by automated decision system determinations”



The Sedona Conference Working Group 11 on Data Security and Privacy Liability

Next meeting: March 18-19, 2020 in Denver

Proposed Principles for AI Implementation

Nicholas Economou James Sherer Jason Baron




Principle 1. Any organization that deploys an AI process that significantly impacts others should consider adoption of policies or procedures that provide for a measure of explainability to the subjects of AI decisions. Anticipate demands for explanations from regulators, the courts,

and the general public Have disclosure policies and procedures in place before you are

asked AI system developers must provide understandable documentation

– its an essential part of the project



Principle 2. The level of explainability required depends on the sensitivity of the AI decision being made. “Sensitivity” includes evaluation of the potential impact

of the decision on data subjects, the data inputs used, and the level of human involvement in the decision-making process




Principle 3. Depending on the sensitivity of the AI decision, explainability includes some form of explanation to individuals impacted by the decision and available remedies.

How AI is being used How AI was developed and “trained” Goal or purpose of AI application Kinds of data used Specific data used in a decision How a decision was made Remedies Statistical data on impact





Principle 4. Organizations should adopt an appropriate form of disclosure to subjects of AI decisions depending on the sensitivity of the decision at issue.Notices Information available upon requestRetained information




Principle 5. The ultimate responsibility of ensuring explainability rests with the individual or organization who uses the AI algorithm to make decisions.Software developersEnd users




Principle 6. At least in circumstances involving the most sensitive AI decisions, a role exists for outside observers to provide input. Public accountability Periodic audits



Business, Legal, and Ethical Aspects of Artificial Intelligence

in Information Governance

Ken Withers, Deputy Executive [email protected]

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Business, Legal, and Ethical Aspects of Artificial