Technology should be a beneficial force in our lives, taking the world in exciting new directions and making us better humans. To ensure this, we need to facilitate a conversation between data technology and the human experience. Keeping social responsibility and ethical behavior in mind when designing AI systems enables us to put the right systems in place to contribute to the society we want, fostering higher levels of cognitive and emotional skills.
Jivan Virdee and Hollie Lubbock explore how to address fairness, accountability, and the long-term effects on our society when designing with data, focusing on four key areas for consideration in this space:
— Responsibility and accountability for machine learning systems
— Fair and transparent data science
— Trust and human-machine collaboration
—Automation and changes in the way we work
Along the way, they cover key issues in creating ethical AI systems and detail how we might go about tackling them and outline further questions that will need to be addressed going forward.
Big thanks to @fjord and @accenturedock for their help and support
Talk by:
https://www.linkedin.com/in/hollie-lubbock-703b77b/
https://www.linkedin.com/in/jivanvirdee/
10. AI will save us/kill us
https://twitter.com/Mark__Zukerberg | https://twitter.com/elonmusk
I think you can build things and the
world gets better, with AI especially,
I’m really optimistic
Until people see robots going down
the street killing people, they don’t
know how to react
Marks understanding of the subject
is limited.
11. “We are morphing so fast that
our ability to invent new
things outpaces the rate we
can civilise them.”
Kevin Kelly, The Inevitable
13. 13
Human Centred
Created from an understanding of human behaviour, motivations, and needs.
Is it the best way to solve that problem?
How can we make their lives better, easier and more fulfilling?
Are the user needs put before the business needs?
Humanity centred
Considering the effect on society as a whole.
What if everyone used your product or service?
What the worst thing that could happen to society because of it?
Does it intrinsically favour one group of people over another?
27. There’s even an
algorithm to check if
you’ll lose your job to
automation
27
https://www.fastcompany.com/3047269/this-calculator-
will-tell-you-if-a-robot-is-coming-for-your-job
35. Problem Complexity
Bounded Open ended
Consequence of Failure
Negligible Critical
Responsibility
Machine Human
Independent
Autonomy
Collaborative
Management by exception
Supervision
Continuous Engagement
36. Bounded Open ended
Negligible Critical
Machine Human
Independent Collaborative
Management by exception Continuous Engagement
Content Moderation
Problem Complexity
Consequence of Failure
Responsibility
Autonomy
Supervision
37. Bounded Open ended
Negligible Critical
Machine Human
Independent Collaborative
Management by exception Continuous Engagement
Movie Recommendation Service
Problem Complexity
Consequence of Failure
Responsibility
Autonomy
Supervision
38. Problem Complexity
Bounded Open ended
Consequence of Failure
Negligible Critical
Responsibility
Machine Human
Independent
Autonomy
Collaborative
Management by exception
Supervision
Continuous Engagement
Three Mile Island Nuclear Plant
39. One of the team,
play to your
strengths
http://humanrobotinteraction.org/journal/
index.php/HRI/article/view/173
40. Creativity is going
to be far more
important in a
future where
software can code
better than we can.
Tom Hulme
52. Data for Democracy
Its my job to understand, mitigate and communicate the
presence of bias in algorithms.
Be responsible for maximizing social benefit and minimizing
harm.
Practice humility and openness.
I will know my data and help future users know it as well.
Make reasonable efforts to know and document its origins and
document its transformation.
Bias will exist. Measure it. Plan for it.
Thou shalt document transparently, accessibly, responsibly,
reproducibly, and communicate.
Engaging the whole community. Do you have all relevant
individuals engaged?
People before data - data scientists should use a question
driven approach rather than a data-driving or methods
approach. Consider personal safety and treat others the way
they want to be treated.
Exercise ethical imagination.
Open by default - use of data should be transparent and fair.
I will not over/under represent findings.
You are part of an ecosystem understand context and
provenance.
Respecting human dignity.
Respect their data even more than your own. Understand
where its sources and think about the consequences of your
actions.
Protecting individual and institutional privacy.
Diversity for inclusivity.
Attention to bias.
Respect for others/persons.
Be intentional as you work to create value.
https://github.com/Data4Democracy/ethics-resources