Technicae novicius: Artificial Intelligence

The information we trust most comes not from strangers, but from friends. From those with whom we have stood in circles spinning yarn and sharing tea. What follows are my notes and research on a given topic – this episode is about AI – as well as some thoughts, questions, and realizations I have had along the way. It’s by no means a polished post, but, by design, a rough snapshot of my knowledge as it develops. My name is Stephanie. I’m a consultant developer here at ThoughtWorks. More importantly, this should be that circle where ideas are exchanged. New thoughts are formed and we sip tea together exploring the questions posed and the dialogue that follows.

• Correctional Offender Management Profiling for Alternative Sanctions (Compas) AI for decision-making in judicial system – http://www.equivant.com/
  • Specifically where is it being used?

• Deep Neural Networks Can Detect Sexual Orientation from Faces – https://osf.io/fk3xr/
  • Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71%of cases for women.
  • LGBT groups objected citing that this tech could be used by anti-LGBT governments and orgs to target people the algorithm identified

• http://www.pewresearch.org/fact-tank/2013/12/09/study-on-twins-suggests-our-political-beliefs-may-be-hard-wired/
  • Big 5 psychological traits:
    • Openness
    • Conscientiousness
    • Extroversion
    • Agreeableness
    • Neuroticism
      
• https://www.theguardian.com/technology/2015/jan/13/your-computer-knows-you-researchers-cambridge-stanford-university
  • “Recruiters could better match candidates with jobs based on their personality; products and services could adjust their behaviour to best match their users’ characters and changing moods.”
    
• https://www.brookings.edu/blog/techtank/2017/07/20/its-time-for-our-justice-system-to-embrace-artificial-intelligence/
  • Alleviate congestion
  • More consistent than humans
  • “But human judgment brings humans failings. Not only are there racial disparities in the sentencing process, but research suggests that extraneous factors like how recently a parole board member ate lunch or how the local college football team is doing can have significant effects on the outcome of a decision.”
    
• Adversarial examples in machine learning – https://blog.openai.com/adversarial-example-research/
  • Target autonomous vehicles by masking street signs with special paint or stickers
• This person keeps coming up: https://www.psychometrics.cam.ac.uk/about-us/directory/michal-kosinski
• Commercial facial and body recognition services – https://www.faceplusplus.com/

Thoughts…

• Do conversations and implementations of AI inherently require conversations about privacy?
• Is “outperforming humans” a good metric of success in AI?
• What’s the error threshold people are willing to tolerate?
• Humans create AI -> Is AI inherently going to have bias?  AI is inherently going to have a bias.

Image: “Lychee & Peach tea with robot.” (CC BY 2.0) by squeezeomatic