As the data-driven landscape rapidly evolves, predictive analytics holds tremendous potential for transformative insights, with predictive models becoming integral to decision-making. However, this immense power demands an equally profound responsibility towards ethical considerations. In this talk, we delve into the crucial interplay between predictive analytics and three paramount ethical aspects: data privacy, bias mitigation, and accountability. We will explore strategies for safeguarding sensitive information, mitigating bias in algorithmic decision-making, and fostering transparency to ensure accountability. Join us to delve into the ethical dimensions of predictive analytics.

1. Ethics in Predictive
Analytics
Presentation by: Bunmi Akinremi
November 24, 2023 17:30

2. Bunmi Akinremi
Machine Learning Engineer,
Kochava
Twitter - @bumie_rose
About Me
Big on AI Research Engineering
Interested in AI policy and economics
Background in Computer Science with Mathematics
Hacks a lot - won over 6 hackathons and participated in
over 20
Community Advocate
Might do a PhD
Poet & Photographer

3. Today's
Agenda
Introduction to Ethics in Predictive Analytics
Historical Context and Current Landscape
Bias and Fairness
Economic and Social Implications
Ethical Frameworks and Policies
Case Studies
Future Trends and Challenges
Role of Data Science Executives
Interactive Discussion or Q&A
Conclusion
What we'll discuss:
Ethics in Predictive Analytics

4. Ethics in Predictive Analytics
Introduction
Predictive analytics combines human skills and
expertise with machine learning and algorithms
to forecast future outcomes and events with a
high level of certainty.

5. Ethics
Ethics can be defined as a set of moral principles that guide
individuals and organizations in making decisions and conducting
themselves in a responsible and fair manner.
Ethics in Predictive Analytics

6. Predictive analytics
Predictive analytics combines human skills with machine learning to forecast
outcomes.
Key ethical concerns: preventing bias, ensuring fairness, and protecting
privacy.
Ethics ensures responsible use and mitigates negative consequences.
Ethics in Predictive Analytics

7. Historical Context
and Current
Landscape
Evolution from 1950s data predictions to
today's sophisticated AI models.
Rise of Big Data in the 2000s amplified
predictive analytics.
Recent AI advancements bring new
ethical challenges in bias and privacy.
Ethics in Predictive Analytics

8. Ethics in Predictive Analytics
Bias and Fairness
Ethical considerations are integral to the responsible use of predictive analytics.

9. Ethics in Predictive Analytics
Bias and Fairness
Data bias: Biases in training data leading
to skewed predictions.
Algorithmic bias: Decisions made by
algorithms reflecting inherent biases.
Societal bias: Systemic inequalities
influencing data and algorithms.

10. Ethics in Predictive Analytics
Detecting Bias
The use of fairness metrics
Through diverse data collection.
Algorithmic transparency

11. Economic and
Social
Implications
Business risks: Loss of trust, legal
penalties, and missed opportunities.
Biased algorithms can harm brand
reputation and financial stability.
Ethical AI practices are crucial for
economic sustainability.
Ethics in Predictive Analytics

12. Ethical
Frameworks
and Policies
EU, ACM, OECD, etc

13. Ethical Frameworks and Policies
Ethics in Predictive Analytics
EU's human-centric AI
guidelines emphasize
respect for values and
rights.
01
ACM Code of Ethics
and Professional
Conduct
02
Implementation
Challenges and EU
Actions
03
Human-Centric Approach,
Transparency, Fairness,
Accountability, Data Governance,
Robustness and Safety, Human
Oversight, Robustness and Safety
Ethical guidelines for computing
professionals, emphasizing ethical
behavior, avoiding harm, honesty,
privacy respect, fairness,
professional development, and
responsibility for one's work.
Until now, we have assumed that
all developers and organizations
understand the ethical
implications of AI. Principles are
highly abstracted.

14. Successes and failures in integrating ethics in
predictive models.
Prewave
Cambridge Analytica
Case Studies
Ethics in Predictive Analytics
01
02

15. Uses Smart Information Systems (SIS) for predictive
risk intelligence in supply chain management,
insurance, finance, and sustainability.
Recognizes the potential ethical concerns related to
the use of SIS and address them, such as a code of
ethics and active engagement with stakeholders
Prewave
Ethics in Predictive Analytics

16. Used data from millions of Facebook users without
their consent to create targeted political
advertisements during the 2016 US presidential
election.
This could have been prevented if the company had
followed ethical guidelines and obtained proper
consent from users before using their data.
Cambridge
Analytica
Ethics in Predictive Analytics

17. Ethics in Predictive Analytics
Future Trends
and Challenges
Emerging technologies in predictive
analytics, such as deep learning, have
the potential to greatly improve
decision-making processes and
provide valuable insights.
Potential for bias in the data
used to train these systems
Lack of transparency and
explainability in these
systems.
Privacy concerns with the use
of personal data in predictive
analytics.
Accountability in AI: A
growing ethical challenge.

18. Role of Data
Science
Executives
Establishing ethical guidelines and
standards in organizations.
Promoting ethical behavior and
decision-making within teams.
Engaging in industry-wide ethical
discussions and standard development.

19. Summary
Ethical considerations are integral to
the responsible use of predictive
analytics.
Addressing biases and ensuring
transparency are key for future
developments.
Data science leaders play a crucial
role in ethical AI implementation.
Ethics in Predictive Analytics
We can begin to look at designing AI
policies with the developers and
users on the table.

20. Food for Thought
Can ethical auditing be an effective tool in enforcing ethical compliance in the
use of predictive analytics?
How can organizations ensure that their use of predictive analytics is not solely
for their own benefit, but also considers the well-being and rights of individuals
and groups?
1.
2.
Ethics in Predictive Analytics

21. Thank you!
Connect with me on Twitter
@bumie_rose
Or send an email
akinremibunmi111@gmail.com
Ethics in Predictive Analytics