AI and Humanity: Can Technology Level the Playing Field for Equality in Society?

Summary Notes of Event 

Hosted by the King's Think Tank

 

19/03/19 @ King's College London 

 

Speakers: Olivier Thereaux (Open Data Institute) and Mark Coté (King’s College London).

Questions: 

  1. How do we remove/eliminate the risk of biases and discriminating algorithms in AI, e.g. on the basis of race, gender, and socioeconomic status?

  2. How are AI/technologies levelling the playing field between the generations (baby boomers, Gen X, Millennials)?

  3. If we assume that job displacement will occur due to automation and AI, what advice do you have for the public and private sectors? How should they best prepare?

  4. How do we prevent data from being siloed and a state being created where the AI market is only shared by a small number of organisations?

Event Description:

Olivier’s reply: 

  • We need biases in AI, otherwise they don’t do very much – so biases are ok. It’s unintended biases that we should watch out for and are not OK in the cases of race, gender, socioeconomic status etc.

  • Need to (1) understand the bias present in AI and (2) how to mitigate unintended biases.

  • It’s dangerously wrong to allocate responsibility to a machine/computer/AI – we shouldn’t anthropomorphise it, we need to get the design right. For that to happen, we need to understand and deal with issues on a human level first.

 

Mark’s reply: 

  • There’s a legal dimension, e.g. illegal to discriminate based on X, Y, Z, but it’s also important for public to understand machine learning and be relatively fluent in it.

    • This would allow people to understand how biases emerge and understand how adjusting data sets and algorithms leads to varying outcomes.

  • Can’t use AI to eliminate discrimination if society and past data is riddled with discrimination

    • E.g. use of AI in recruitment based on past practice can propagate discrimination.

Q1) Eliminating Bias:

Mark: 

  • Not sure it would.

 

  • Olivier: 

  • Different generations have different attitudes to data and privacy and so will be affected differently, though this is unlikely to level the playing field.

    • E.g. millennials are most naïve and open, growing up with social media and putting everything about them “out there”

    • Gen Z are more private, perhaps learning the lessons from millennials

    • Gen X have always been more private as they were older during the birth of social media and reluctant to share.

    • Baby boomers too old to engage initially.

Q2) Levelling the generational playing field:

Olivier: 

  • This is a big assumption to make, since estimates vary wildly and no one really knows how many jobs will disappear. Jobs will be lost but will also be created.

  • But, the typical response by most governments has been (1) ensure they have the right infrastructure and increase flows of data (2) train more data scientists (3) reskill workers.

    • Issue with (3) is that we don’t know what industries are safe from automation yet, so we don’t know what to reskill people into yet. 

    • We should definitely wargame and prepare for changes across industries, (3) is probably the best safeguard.

 

Mark: 

  • We should reconceive AI as a narrowapplication. It does one thing very well; it has a superhuman performance e.g. Alphazero, but isn’t as good at other tasks. 

  • Many jobs would still need humans to do the rest of the work.

 

Olivier: 

  • A distinction needs to be made between automation and augmentation. 

    • We’ve used horses in farms historically and then we used machines in manufacturing and now we’re using further iterations of this in other industries. This augments the worker, doesn’t replace them.

  • Tech so far has mostly brought augmentation.

  • White collar jobs are at risk e.g. junior lawyers, as well as menial tasks.

  • Centaur model is still possible, for certain things, and would lead to a healthy future if implemented.

 

Mark: 

  • We will see a changing of roles and an improved capacity to work in an interdisciplinary environment. Key is to participate in augmentation.

Q3) Countering Work  Automation:

Olivier: 

  • There are monopolies forming, e.g. FAGAM (Facebook, Apple, Google, Amazon, Microsoft).

    • But, data is non-rivalrous: creates more, or doesn’t lose, value when shared.

    • If we allow other industries to share data and make use of it then we have value creation and then no need to break up monopolies. If the data is kept by a monopoly then the urge to break up the companies will be greater.

 

Mark:

  • The value of data increases and aggregates the more it’s shared, when going through various processes and mixed with other data.

  • It’s never exhausted over time, so we should be optimistic about the value of data and its possible uses.

  • But, FAGAM + Tencent + Baidu etc. dohave a disproportionate control over data.

  • We currently get the tip of the value that our data has when we give it to them, but they reap the rest and aggregate that value with everyone else’s data.

  • Mark’s Q: if data is non-rivalrous then why is there still a narrow set of applications for our data?

 

Olivier: 

  • GDPR gives us certain rights (1) accessibility (to see data) (2) to be forgotten (to delete data) (3) portability (to move data, e.g. to a competitor).

    • (3) is not well known, the least used, and the least enforced.

Q4) Preventing Data Silos:

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