Glantus Presentation Slides - Ethical Data Science - BoI Analytics Connect 2018
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The document discusses the evolution of data science, highlighting key milestones from its origins in the 1960s to its contemporary applications and challenges, particularly with bias and ethics. It emphasizes the importance of understanding and mitigating bias in data to ensure fair and ethical data science practices, especially in industries like finance where regulatory compliance is critical. The Glantus platform is introduced as a solution for automating data preparation and analytics to support fair lending practices.
Glantus Presentation Slides - Ethical Data Science - BoI Analytics Connect 2018
- 1. P R E S E N TAT I O N T E M P L AT E CORPORATEJoe Keating Ethical Data Science - with Big Data, comes Big Responsibility. Bank of Ireland - Analytics Connect 20.09.2018
- 2. The Origins of Data Science 1962 ⢠John W. Tukey writes in âThe Future of Data Analysisâ that.. âdata analysis is intrinsically an empirical scienceâ. ⢠He Later published âExploratory Data Analysisâ, arguing that more emphasis needed to be placed on âusing data to suggest hypotheses to testâ. 1974 ⢠Peter Naur publishes the âConcise Survey of Computer Methodsâ including the following definition of data science: ⢠âThe science of dealing with data, once they have been established - while the relation of the data to what they represent is delegated to other fields and sciences.â 1977 ⢠The International Association for Statistical Computing (IASC) is established with the following mission: ⢠âto link traditional statistical methodology, modern computer technology, and the knowledge of domain experts in order to convert data into information and knowledge.â 1996 ⢠Members of the International Federation of Classification Societies (IFCS) meet in Kobe, Japan, for their biennial conference. ⢠For the first time, the term âdata scienceâ is included in the title of the conference. 2009 ⢠Nathan Yau writes in âRise of the Data Scientistâ that.. âWe're seeing data scientistsâpeople who can do it allâ emerge from the rest of the packâ. 2011 ⢠Harlan Harris writes in âData Science, Mooreâs Law, and Moneyballâ that.. âWhat Data Scientists do has been very well covered, and it runs the gamut from data collection and munging, through application of statistics and machine learning and related techniques, to interpretation, communication, and visualization of the results.â
- 3. Traditional Data Science â˘Targeted advertising based on our online activity, whether itâs a YouTube pre-roll ad or a targeted article on Facebook. Deep learning â˘Scales to much larger volumes and gives better results. However, we donât necessarily know how it works. You canât open up the lid and look inside a black-box model. Where is Data Science today? ⢠At the coalface of digital transformation. ⢠The opportunity for achieving social good within the field of data science is huge. ⢠Data has become one of the most valuable commodities in the global economy. Typical Application Emerging Techniques
- 4. What About Bias and Ethics? ⢠A central challenge in building a fair model is to quantify some notion of âfairnessâ. ⢠Group vs. Individual Fairness ⢠Group fairness is the requirement that different groups of people should be treated the same on average. ⢠Individual fairness is the requirement that individuals who are similar should be treated similarly. ⢠Sample Bias vs. Label Bias in your Data ⢠Label bias occurs when the data-generating process systematically assigns labels differently for different groups (i.e. Studies show that men with beards drink more = label bias). ⢠Sample bias occurs when the data-generating process, samples from different groups in different ways (i.e. More people are tested for drink driving in Dublin than elsewhere = sample bias).
- 5. What is an Example of Bias? In the financial industry, biased data may cause results that offend the United States Equal Credit Opportunity Act (fair lending). This law, enacted in 1974, prohibits credit discrimination based on race, color, religion, national origin, sex, marital status, age or source of income. While lenders will take steps not to include such data in a loan decision, it may be possible to infer race in some cases using a zip code, for example.
- 6. The Impact - Consumer Financial Protection Bureau Federal law that places regulation of the financial industry in the hands of the government
- 7. How do we eliminate Bias? ⢠To mitigate bias, data scientists need to understand the data and its contexts before they even begin modelling algorithmic patterns. ⢠If bias is present in the data collected, the algorithm will carry this forward. ⢠Model outcomes will become more neutral if an algorithm is trained on data that is pre-processed to minimize bias. There is no silver bullet measurement which is guaranteed to detect unfairness, choosing an appropriate definition of model fairness is task- specific but should always be underpinned by an ethical code of conduct and transparency.
- 8. âData made simpleâ Glantus provides a unique data platform, using our own IPR to enable organisations to discover and optimise the value of their business data.
- 9. 1. Connect to Data from any Digital source - configure Validation, Classification and Transformation Rules, if necessary 2. Apply out-of-the-box Algorithms that are Industry, Solution Specific to suit your needs and/or Create your own â simply 3. Automate end-to-end Workflows to eliminate manual effort and add value to your business through proactive Actions 4. Empower the Business to self-serve their own Data needs How it Works.
- 10. The Glantus platform provides an automated, end-to-end solution to compile data and perform analytics to support the requirements mandated by Financial Regulation. Solution to Apply the Rules of Fair Lending Data Preparation ⢠Connect to any and all digital systems and sources for aggregation and transformation in line with the regulatory requirements identified ⢠Automated generation of Universal Loan Identifiers (ULI) and check digits ⢠Automated validation and notifications based on user defined rules Analytics ⢠Automate the consolidation of qualitative and quantitative data elements ⢠Produce Risk Scores at individual and cumulative levels across all businesses within the organization ⢠Examine activity by region (e.g., MSA) and branch (e.g., origination activity and loan approval rates) as a function of race, ethnicity, or gender across LOBs and entities Automation ⢠Automated digital communications based outcome on the Analytics: ⢠Internal, External Systems ⢠Email ⢠SMS ⢠Pro-active Monitoring and Alerting based on Real-Time activity
- 11. Glantus Data Stream ERP Data Web Store Data Master and Transactional Data feed Mimic Orders streaming form web store Twitter feed monitoring for keywords Workflow Modeler Structured DB Actions ⢠Tweet ⢠SMS ⢠eMail ⢠DB Update Typical Use Case
- 12. Your data becomes your asset. Contact us @ www.glantus.com With
Editor's Notes
- #2: Initial introductions.
- #9: Focus on the fact that the end-to-end platform is unique (on a global level) and emphasize that this is our IPR.
- #10: One single platform to Connect, Analyse and Optimise the visibility and use of data within the organisation.
- #11: Organic Growth through Dublin, Gloucester and Katowice. New York through Acquisition. Dubai, Sing and Sydney based on Alliance and Partnerships.