93469 - Human Data Science

Learning outcomes

This course aims to demonstrate ways in which human-centric perspectives lead to new approaches to analyzing, evaluating and understanding machine learning (ML) models. For instance, a conventional focus on traditional computational criteria may be insufficient to identify suitable ML-based systems. Instead, reflecting on the training data and their role and quality, as well as relying on tight action-feedback loops that engage humans, may bring to modified model behaviors that are beneficial to users, helping them to understand those models as more than simple black boxes. Misuses of big data, lack of specific attention to their quality and absence of humans in the loop are discussed with the aim to understand what makes Data Science different.

Course contents

Human data science integrates the study of data science with breakthroughs in humans and algorithms to advance our ecosystem of knowledge, and help everyone make better, more insightful decisions. Case studies will be presented to illustrate the following:

- Datification/Interaction with special data (e.g., medical)

- Predictive/Prescient intelligence

- Data Thinking/Data deluge

- Data deluge and profusion

- Human-Machine collaboration workflow

- Models for humans and machines - Machine/Deep learning

- Human factors and ELSI

Readings/Bibliography

Scientific papers and reports delivered by the Lecturer during the course

Teaching methods

Class lectures and projects discussion and development

Assessment methods

Aim of the examination process is to assess if adequate skills have been acquired by students in this specific field. The exam typically proceeds as follows. The lecturer proposes, also with the help of seminars delivered by external guests, possible case studies in the field of human data science. Students choose one of those cases and develop it until a correspondent project is developed. In the end, students are required to deliver a public talk where the developed project is analyzed and its characteristics discussed.

The score assigned to the project depends equally on three main factors: originality of the proposal, correctness of execution, and adherence to the basic principles developed during the course.

As to students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible ( https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students ) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

Teaching tools

Departmental networked lab and applications

Office hours

See the website of Marco Roccetti