The document discusses algorithms and their potential for bias. It describes the UnBias project, which aims to develop tools to help mitigate unjustified bias in algorithmic systems. The tools include ones to raise awareness of online environments for young users, help users navigate online, and help providers understand user concerns. The document also presents two case studies, one on recommender algorithms and fake news, and another on unintended discrimination from personalization algorithms. It poses questions about search engine results, personalization transparency, and user control over personal data.

1. Algorithm Mediated Online
Information Access (AMOIA)
28TH OCTOBER 2017
MOZFEST 2017

2. UnBias: Emancipating Users Against Algorithmic
Biases for a Trusted Digital Economy
User experience of algorithm driven internet platforms
and the processes of algorithm design

3. UnBias: Emancipating Users Against Algorithmic
Biases for a Trusted Digital Economy
Mission: Develop co-designed recommendations for design, regulation and
education to mitigate unjustified bias in algorithmic systems.
Aim: A ‘fairness toolkit’ consisting of three co-designed tools
◦ a consciousness raising tool for young internet users to help them understand
online environments;
◦ an empowerment tool to help users navigate through online environments;
◦ an empathy tool for online providers and other stakeholders to help them
understand the concerns and rights of internet users.

4. Limited resources assignment problem:
Choose your favourite character to play
Each character can be played by only one player

5. Satisfaction Distribution

6. The algorithm decision principles
A1: minimise total disparity
while guaranteeing at least 70%
of maximum overall satisfaction
A2: maximise the minimum
individual satisfaction while
guaranteeing at least 70% of
maximum overall satisfaction
A3: maximise overall
satisfaction
A4: maximise the minimum
individual satisfaction
A5: minimise total disparity

7. Here is one we did earlier with CS students
 A1: minimise total distance while
guaranteeing at least 70% of
maximum possible utility
 A2: maximise the minimum
individual student utility while
guaranteeing at least 70% of
maximum possible total utility
 A3: maximise total utility
 A4: maximise the minimum
individual student utility
 A5: minimise total distance
Most preferred
Least preferred

8. Case Study 1: The role of recommender
algorithms in online hoaxes and fake news

9. Questions to consider
1. How easy is it for a) individual users and b) groups of users to influence the order to
responses in a web-search?
2. How could search engines weight their search results towards more authoritative results
ahead of more popular ones? Should they?
3. To what extent should web search platforms manually manipulate their own algorithms
and in what instances? NB Google has made a number of adjustments re anti-Semitism
etc. and places a suicide help line at the top of searches about how to kill oneself.
4. To what extent should public opinion influence the ways in which platforms design and
adjust their autocomplete and search algorithms?
5. What other features should and should not have a role in influencing the design of
autocomplete and search algorithms?

10. Case Study 2: Unintended algorithmic discrimination
online – towards detection and prevention
Personalization can
be very helpful
However there are concerns:
1. The creation of online echo chambers or filter
bubbles.
2. The results of personalisation algorithms may be
inaccurate and even discriminatory.
3. Personalisation algorithms function to collate and
act on information collected about the online user.
4. Algorithms typically lack transparency.

11. Questions to consider:
1. What is your response to this comment from Mark Zuckerberg to explain the value of
personalisation on the platform? “A squirrel dying in front of your house may be more
relevant to your interests right now than people dying in Africa”
2. What (legal or ethical) responsibilities do internet platforms have to ensure their
personalisation algorithms are 1) not inaccurate or discriminatory and 2) transparent?
3. To what extent should users be able to determine how much or how little personal data
internet platforms collect about us?
4. To what extent would algorithmic transparency help to address concerns raised about the
negative impacts of personalisation algorithms?
5. Is there any point to algorithmic transparency? What might be some useful alternatives?

12. http://unbias.wp.horizon.ac.uk/