Image courtesy Unsplash / @WilhelmGunkel
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Week 9/S1/2022
Transparency:
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FINAL SLIDES!
Decisions & Processes
Marc of Computing and Information Systems Centre for AI & Digital Ethics
The University of Melbourne
marc.cheong [at] unimelb.edu.au
Learning Outcomes
1. Distinguish between transparency and explainability, closely-related concepts in AI ethics.
2. Understand how automated decision-making (ADM) systems require transparency at every stage.
3. Understand how tech companies approach the issue of transparency, as well as concerns that are raised by stakeholders, in two areas where AI systems are deployed: social media ads and criminal justice.
4. Understand how big data research can – either positively or negatively – affect their data subjects, and why transparency is important when conducting such studies.
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Related Reading
This module has two readings corresponding to the two broad themes within (plus an optional study). Recap: screenshot belowJ
– COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
Transparency: what’s it all about.
Automated decision-making (ADM) systems: transparency at every stage?
Current issues in transparency and stakeholders’ concerns, social media advertising systems
criminal justice AI systems
Data science research and transparency: the cases of Cambridge Analytica and Covid19 Trends.
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Transparency: What’s it all about?
Image courtesy Unsplash / @WilhelmGunkel
A note on nomenclature (and focus)
Some academics use the term transparency to describe the “inner workings” of algorithms (e.g. Loi et al, 2020: https://link.springer.com/article/10.1007/s10676-020-09564-w)
To clarify, the focus of this module is on the overarching processes and decision-making of an algorithmic system.
We include the technical considerations about interpretability of the algorithms themselves, counterfactual analysis, etc under the banner of explainability.
(Our very own is an expert in this field).
Image source: Eliiza
– COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
Transparency? (1/2)
Source: UNESCO news release, quoting : https://en.unesco.org/news/privacy-expert-argues-algorithmic- transparency-crucial-online-freedoms-unesco-knowledge-cafe 7
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Transparency? (2/2)
Jake Goldenfein, ‘Algorithmic Transparency and Decision-Making Accountability: Thoughts for buying machine learning algorithms’ in Office of the Victorian Information Commissioner (ed), Closer to the Machine: Technical, Social, and Legal aspects of AI (2019). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3445873
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Automated decision-making (ADM) systems: Transparency at every stage?
Image courtesy Unsplash / @WilhelmGunkel
Disclaimer: I am not a lawyer
The information provided in this mini-lecture is summarized from various sources to explain how transparency is a requirement not only for the algorithms, but also the contexts surrounding their implementation.
This lecture won’t make you an expert in administrative decision-makingJ
Image source: CAPCOM / Cinemablend. – COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
Source: Administrative Review Council, Automated Assistance in Administrative Decision Making: Report to the Attorney General (Report No 46, November 2004) (‘2004 Report’)
https://www.ag.gov.au/legal-system/publications/report-46-automated-assistance-administrative-decision-making-2004 The 2004 Report was ahead of its time (emphases below are mine)
P27: “Safeguards built into the system are only asking relevant questions, telling customers why questions are being asked (which makes the decision-making process more transparent) and recording and explaining to a customer the reason for a decision”
P43: “Expert systems’ ability to provide an audit trail of the administrative decision-making processes they are involved in is important to the administrative law values of transparency, fairness and efficiency. “
P45: “A good system of internal review is one which is transparent in process and affords a quick,
inexpensive and independent review of decisions. Such a system is beneficial both to applicants and agencies”. (citing Administrative Review Council 2000)
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, ‘Algorithms: transparency and accountability’, Phil. Trans. R. Soc. A.3762017035120170351. (2018). https://royalsocietypublishing.org/doi/10.1098/rsta.2017.0351
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High-level view of AI systems (OECD, 2019)
Source: OECD (2019), Artificial Intelligence in Society, OECD Publishing, Paris, https://doi.org/10.1787/eedfee77-en.
Figure: https://www.oecd-ilibrary.org/sites/8b303b6f-en/index.html?itemId=/content/component/8b303b6f-en#figure-d1e976
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Current issues in transparency:
Social media advertising systems
Image courtesy Unsplash / @WilhelmGunkel
Consider Facebook’s group of products
If we use Instagram, Facebook, WhatsApp, how many of us actually read the TOS?
Source: Scherker (2017) h\ps://www.huffpost.co m/entry/facebook- terms- condi_on_n_5551965
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Consider ads: is this ‘transparent’?
Let’s say I’ve been shown an ad (sponsored post) on Facebook. As a consumer, I want to know WHY.
(Image sources: Facebook).
I go to the TOS, then find ‘Ads’, then find ‘Data Policy’… Which takes me to another page…
Which scares me.
Let’s try another method – go to the ad, and click on the menu.
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Consider ads: is this ‘transparent’?
Let’s say I’ve been shown an ad (sponsored post) on Facebook. As a consumer, I want to know WHY. (Image sources: Facebook)
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Consider ads: is this ‘transparent’?
Case study thanks to ,
( Institute for Human Development, Germany)
https://psyarxiv.com/ea28z/ – Lorenz-Spreen et al (2021)
“At present, the platforms’ transparency measures offer “nominal transparency”, with no real regard for whether people actually can easily access, read and gain insight into the information held about them and whether this transparency in name foster users’ autonomy.
“Aiming for effective transparency—which demonstrably enables users to understand what platforms do with their data and what users’ choices imply, and to then translate this knowledge into measurable behaviour—is an important step towards more acceptable business practices and towards regaining some of the lost autonomy for users (e.g., by prompting people to adjust their privacy settings; Parra-Arnau et al., 2017)
– COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
Current issues in transparency:
Criminal justice AI systems (and COMPAS revisited)
Image courtesy Unsplash / @WilhelmGunkel
Criminal Justice and AI systems
You have encountered the following examples in your explora_on of AI ethics. (Images are from Wikipedia)
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Reading: Rudin (2019) on COMPAS
The focus of this case study is not about the technical explainability for the underlying algorithms, etc.
… but about the transparency of the processes and decisions involved.
Take COMPAS – and its decision making assump_ons, in prac_ce (Rudin 2019)
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Asaro (2019) on Pred the same vein we consider COMPAS; cf PredPol in Asaro (2019)
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Ferguson (2017) on Pred the same vein we consider COMPAS; cf. PredPol – Ferguson (2017) provides a legal perspec_ve.
Issues at all stages: crime stats; data dossiers; personal/cultural bias; data entry/analysis; tech complexity; financial/IP interests; audi_ng; metrics…
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Reflection.
AI systems in justice/policing causes serious effects on people’s freedom and status under the law.
The legal perspective of AI ethics gives us another perspective on the need for transparency.
“Transparency is difficult, but it matters to a functioning predictive system that deals with individuals’ lives and liberty” (Ferguson, 2017).
How do we start fixing the issues?
E.g. for PredPol – auditing, public release of metrics, training (Ferguson, 2017).
…or not use PredPol to begin with?
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Big Data Research & Social Media: From Elections to Pandemics
Image courtesy Unsplash / @WilhelmGunkel
Reading: Walsh (2019)
– COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
Reading: Walsh (2019)
Reflections for transparency in social media and big data research, by Walsh (2019):
1. “The first recommendation is that we may need to take into account not just the impact on the individual under study but the broader impact any experiment might have on society…”
2. “The second recommendation is that ethics approval may be needed…”
3. “The third recommendation is that subjects of any experiment may need to be informed directly after the study about the results and their participation…”
– COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
Conclusion.
Big data research on social media invokes many concerns – privacy (can the user opt out of the ‘researchers gaze’); autonomy (does the research make the users do things they won’t otherwise?); wellbeing (does the research have the potential to change mood/health outcomes?)… are these clear to the users?
Concluding food for thought: is this ethical?
Sources: https://spectrum.ieee.org/the-human-os/telecom/wireless/facebook-google-data-publics-movement-covid19; Google – COMP90087 – Semester 1, 2022 – © University of Melbourne 2022
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