Re: [i4j Forum] Re: G7 leaders call for ‘guardrails’ on development of AI

Even with "black boxes" one can still do transparency on:

* the data collection procedures (how do you collect the data? how do you
obtain consent?)
* the data curation procedures (how do you correct for errors or things you
don't want to teach the machine?)
* the review of the AI outputs (how do you assess if what the AI is
outputting is socially acceptable? correct/accurate if that's a
qualification? etc.)
* the review of the AI impacts on people (how do you review to confirm the
AI isn't causing unintentional harm?)
* the review of the AI's biases (all machines will have biases, and even
correcting for socially unacceptable biases will introduce other biases,
how do you review and make changes as appropriate?)

Which could be posted publicly as what does this organization do to answer
and address these important areas.

Hope this helps,

-d.


On Fri, May 19, 2023 at 4:55 PM David Michaelis <michaelisdavid@yahoo.com>
wrote:

> Hi David
> Interesting challenges in your principles.
> How can one ask for transparency when the black box is not transparent??!
> At this stage there are too many unknowns in this Golem we have built.
>
>
> Sent from Yahoo Mail for iPhone
> <https://mail.onelink.me/107872968?pid=nativeplacement&c=Global_Acquisition_YMktg_315_Internal_EmailSignature&af_sub1=Acquisition&af_sub2=Global_YMktg&af_sub3=&af_sub4=100000604&af_sub5=EmailSignature__Static_>
>
> On Saturday, May 20, 2023, 6:26 am, David Bray, PhD <
> david.a.bray@gmail.com> wrote:
>
> Hi Paul - back in 2019 and 2020, Ray Wang and I published the following
> with MIT Sloan Management Review re: 5 steps to People-Centered AI:
>
>
> https://mitsloan.mit.edu/ideas-made-to-matter/5-steps-to-people-centered-artificial-intelligence
>
> *1. Classify what you're trying to accomplish with AI*
>
> Most organizations are pursuing initiatives to do the following:
>
>    - Automate tasks with machines so humans can focus on strategic
>    initiatives.
>    - Augment — applying intelligence and algorithms to build on people’s
>    skill sets.
>    - Discover — find patterns that wouldn’t be detected otherwise.
>    - Aid in risk mitigation and compliance.
>
> *2. Embrace three guiding principles *
>
> *Transparency. *Whenever possible, make the high-level implementation
> details of an AI project available to all involved. This will help people
> understand what artificial intelligence is, how it works, and what data
> sets are involved.
>
> *Explainability. *Ensure employees and external stakeholders understand
> how any AI system arrives at its contextual decisions —specifically, what
> method was used to tune the algorithms and how decision-makers will
> leverage any conclusions.
>
> *Reversibility.* Organizations must also be able to reverse what deep
> learning knows: The ability to unlearn certain knowledge or data helps
> protect against unwanted biases in data sets. Reversibility is something
> that must be designed into the conception of an AI effort and often will
> require cross-functional expertise and support, the experts said.
> *3. Establish data advocates*
>
> When it comes to data, the saying, “garbage in, garbage out” holds. Some
> companies are installing chief data officers
> <https://mitsloan.mit.edu/ideas-made-to-matter/make-room-executive-suite-here-comes-cdo-2-0>
> to oversee data practices, but Bray and Wang said that’s not enough.
>
> The pair suggested identifying stakeholders across the entire organization
> who understand the quality issues and data risks and who will work from a
> people-centered code of ethics. These stakeholders are responsible for
> ensuring data sets are appropriate and for catching any errors or flaws in
> data sets or AI outputs early.
>
> “It’s got to be a cavalry — it can’t be relegated to just a few people in
> the organization,” Bray said. One approach the experts suggested is to
> appoint an ombuds function that brings together stakeholders from different
> business units as well as outside constituents.
> *4. Practice “mindful monitoring”*
>
> Creating a process for testing data sets for bias can help reduce risk.
> Bray and Wang suggested identifying three pools of data sets: Trusted data
> used to train the AI implementation; a queued data pool of potentially
> worthwhile data; and problematic or unreliable data. And data should be
> regularly assessed — for example, whether previously approved trusted data
> is still relevant or unreliable, or if queued data has a newfound role in
> improving the existing pool of trusted data for specific actions.
> *5. Ground your expectations*
>
> Managing expectations of internal and external stakeholders is crucial to
> long-term success. To gain consensus and keep focus on a people-oriented AI
> agenda, organizations should ask and answer such questions as: What is our
> obligation to society? What are the acknowledged unknowns? What are
> responsible actions or proactive things we can accomplish with AI
> implementations, and what are the proper safeguards?
>
> In the end, it makes sense to approach AI as an experimental learning
> activity, with ups, downs, and delays. “There will periods of learning,
> periods of diminished returns, and [times when] the exponential gain
> actually benefits the organization,” Bray said. “You need to be grounded
> and say, ‘This is how we’ve chosen to position ourselves.’ It will serve as
> your North Star as you move towards the final goal.”
>
> On Fri, May 19, 2023 at 2:26 PM Paul Werbos <pwerbos@gmail.com> wrote:
>
> Thanks, Timothy, for updating our awareness and asking us to think about
> the implications:
>
> On Fri, May 19, 2023 at 9:39 AM Timothy Holborn <timothy.holborn@gmail.com>
> wrote:
>
> I was alerted to: https://twitter.com/FT/status/1659481447428751360
>
> “We reaffirm that AI policies and regulations should be *human centric*
> and based on nine democratic values, including protection of human rights
> and fundamental freedoms and the protection of privacy and personal data.We
> also reassert that AI policies and regulations should be risk-based and
> forward-looking to preserve an open and enabling environment for AI
> development and deployment that maximises the benefits of the technology
> for people and the planet while mitigating its risks,” the ministers’
> communique stated.
>
> Source is from:
>
> https://g7digital-tech-2023.go.jp/topics/pdf/pdf_20230430/ministerial_declaration_dtmm.pdf
>
> FWIW: personally, i think of many of these requirements as 'safety
> protocols', but am open and interested to hear the views of others...
>
>
> My views: I see an analogy to great pronouncements and even goals on
> climate change a few years ago,
> WITHOUT the kind of groundwork needed to get the great goals implemented.
> Useful implementation is MORE URGENT here,
> because the worst case pathways to extincion run even
> faster with internet/AGI/IOT than with climate. It is far more difficult,
> because the physical details are harder for people to understand. (For
> example, H2S in atmosphere is a lot easier to visualize than QAGI.)
>
> The design requirements are simply not under this open discussion. I hope
> Jerry's effort can help close this life or death gap.
>
>
>
>
> Cheers,
>
> Timothy Holborn
> www.humancentricai.org
>
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