Verantwortung der Informatik – Accountability In AI (AIAI 2021)

[Slides]

[Slides]

Federated Learning promises inherent data privacy among other things as compared to centrally trained ML models. This is supposedly done by training local models distributedly and updating a global model every so often. The data used for training stays local and is hence “safe”, or is it? Can we reconstruct the data used for training from the local model’s weights?

Literature:

• Breaking Privacy in Federated Learning
• A survey on security and privacy of federated learning
• Secure, privacy-preserving and federated machine learning in medical imaging

Biases in data can lead to different behaviour resulting from various ethnic and/or socio-demographic subpopulations. Especially for applications in medicine and healthcare, this can have critical effects on underrepresented groups. Which metrics or methods can be used to detect biases in data collection, pre-processing and/or model performance? What can be done to remove biases? What consequences can biases have?

Literature:

• Fairness in Deep Learning: A Computational Perspective
• Ethical Machine Learning in Health Care

Project Ideas:

• Compare how Covid-19 genomes are sampled between different countries and which consequences this could have on public health
• Application of fairness metrics on real-world data

To ensure quality for a product, so-called Audits are carried out to check if the product meets certain standards and regulations. For AI for health, the requirements for safety and efficacy are particularly strict and difficult to evaluate. These evaluations include the analysis of model performance, privacy, robustness, fairness and bias, and interpretability among other things. Which processes could the evaluation part of an audit include? Which regulations or guidelines are available? How does AI for health differ from other applications regarding the evaluations?

Literature:

• ML4H Auditing: From Paper to Practice
• Ethical Machine Learning in Health Care
• Ethics and governance of artificial intelligence for health

Project Ideas:

• Investigation of audit guidelines on a use-case (eg. Antibiotic Resistance Prediction) or existing project
• Analyze which issues occur for auditing in AI for medicine/health with special focus on your use-case
• (Optional) Propose methods/tools/metrics for analyzing evaluation and/or life cycle steps

Most popular (social media) websites and apps use recommender systems to individually filter content and provide users with suggestions of movies to watch, news articles to read, music to listen to, etc. Suggestions are based on previous user interactions and optimized to match the users' interests, maximizing user engagement. By providing a constant feed of interesting content recommender systems can lead to the excessive use (addiction) of internet applications. On the other hand, recommender systems also create echo chambers, reinforcing the opinions of users, by only showing content that agrees with a user's preexisting opinion. These virtual echo chambers or "bubbles" make critical discourse much harder, because a ground truth, that both parties can agree on no longer exists. How can recommender systems be built to be less addicting, while still providing relevant content? How to pop the "bubbles"?

Literature:

• Excessive use of online video streaming services: Impact of recommender system use, psychological factors, and motives
• Recommender systems and their ethical challenges
• How TikTok recommends videos #ForYou

Project Ideas:

• First, use a toy dataset to implement a conventional recommender system as a control condition. Run a small user study to measure user engagement and satisfaction. Then implement an improved version that is less addicting or not as prone to sustaining bubbles. Test your hypothesis by running a study with your improvements.

Big, state-of-the-art neural networks need big datasets for training. How does this fit with the principles of the European data protection laws (GDPR)? How can we ensure data protection and progress in AI at the same time? What restrictions are already imposed on AI research and products by the GDPR? How can requirements such as the principle of data economy (Grundsatz der Datensparsamkeit) and the right to be forgotten be implemented? How can we ensure the privacy of uninvolved third parties such as relatives, which share parts of their genome?

Literature:

• Big data, artificial intelligence, machine learning and data protection
• Catching Criminals With Their Relative's DNA

Project Ideas:

• choose one or more requirements of the GDPR and implement two or more ideas to fulfill these requirements

With the development of artificial intelligence (AI) the vehicles are becoming more and more autonomous. It implied the necessity of setting Legal and Regulatory Statements. The need to answer the following questions arose: how to test and develop driverless vehicles, who takes responsibility when an accident happens, what are the manufacture’s obligations, to name a few. The presentation would include a discussion of the above issues and presenting the approach in different countries

Literature:

• Global Guide to Autonomous Vehicles
• Legal and regulatory implications of Artificial Intelligence. The case of autonomous vehicles, m-health and data mining

Nowadays, machine learning (ML) techniques are gaining more and more popularity because of their performance. With them, we are able to model complex phenomena receiving high accuracy of results. One of the limitations of ML models is the fact the mean value of the output is estimated. It means that we do not know how our model is confident about the result. Consequently, we - end users- are not sure if the model is trustworthy and safe. To overcome that problem the uncertainty of the prediction can be estimated. Uncertainty tells us with the given probability how far from the mean value the real value is expected to be. Uncertainty estimation allows users to decide if they can trust the model and when there is a need for an additional human decision.

Literature:

• Uncertainty in Deep Learning
• Uncertainty Quantification in Deep Learning
• Importance of being uncertain

Project Ideas:

• Implement algorithm for different uncertainity estimation methods and compare the results for one of the following input data: tabular data, images, or text.

Artificial Intelligence (AI) is attracting the attention of more and more specialists, not only computer scientists and statisticians but also medical personnel, engineers, and economists. AI enables the modeling of complex phenomena receiving high accuracy of results. One of the limitations is the fact that AI models are “black boxes”. It means that it is difficult to explain the relationship between the input features and the output. Consequently, we - end users- are not sure if the model is trustworthy and safe. To overcome this problem different algorithms and approaches have been developed to explain predictions. It also allows estimating which features have the highest impact on the result. As a result, the users can assess the outcome with state-of-the-art knowledge and discover new patterns in an analyzed phenomenon.

Literature:

• Interpretable machine learning. A Guide for Making Black Box Models Explainable
• Explainable AI: A Review of Machine Learning Interpretability Methods
• Explainable Artificial Intelligence: a Systematic Review

Project Ideas:

• Implement algorithm for different explainable AI methods and compare the results for one of the following input data: tabular data, images, or text.

"Dual use goods are products and technologies normally used for civilian purposes but which may have military applications." [1] Like many technologies, AI has the potential to be used in military applications. Be it object detection used in autonomous killer drones to localize targets or deep fakes used to spread misinformation online. How can military use be avoided? Which areas should not be researched? Do we need a ban on "weaponizable" AI such as facial recognition?

Literature:

• [1] Dual-use trade controls
• DUAL-USE: Berücksichtigung militärischer Anforderungen bei der zivilen Entwicklung neuer Technologien
• YOLO Creator Joseph Redmon Stopped CV Research Due to Ethical Concerns
• Twitter thread
• Dodging Attack Using Carefully Crafted Natural Makeup

Project Ideas:

• Implement a possible defense for a dual-use AI technology (see: Dodging Attack Using Carefully Crafted Natural Makeup)

Training deep neural networks is energy-intensive. It is estimated that training GPT-3 required about 190 000 kWh. [2] Are advances in AI worth this environmental cost? Which experiments are worth running? How can we reduce the energy consumption or the number of experiments that need to be run?

Literature:

• [2] AI me to the Moon... Carbon footprint for 'training GPT-3' same as driving to our natural satellite and back
• Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models
• On the Energy Consumption of Deep Learning Workloads

Project Ideas:

• Compare the energy consumption of different ML algorithms on the same problem
• Implement ideas to reduce energy consumption and benchmark them

Synthetic Media is a catch-all term for the artificial production, manipulation, and modification of data and media by automated means. Synthetic media as a field has grown rapidly since the creation of generative adversarial networks, primarily through the rise of Deepfakes as well as music synthesis, text generation, human image synthesis, speech synthesis, and more. While this technology may bear positive effects, like cutting cost in production, it's rather obvious that it's possibilities are rather concerning. From misrepresenting well-known politicians, and thus misinforming the public, to social hacking. So far potential users needed to have at least some proficiency in coding, but apps like reface show that this technology is getting more accessible to non-experts. How doomed are we?

Literature:

• Synthesizing Obama: learning lip sync from audio
• The Creation and Detection of Deepfakes: A Survey
• Everybody's Talkin': Let Me Talk as You Want
• Iterative Text-based Editing of Talking-heads Using Neural Retargeting
• BANK ROBBERS STEAL $35 MILLION BY DEEPFAKING BOSS’S VOICE

Project Ideas:

• Make someone say what you want
• Let a news anchor tell some fake news

• What is the topic?
• How is it defined?
• Are there multiple, different definitions?
• Why is it important?

• Present a method/paper/tool which addresses the problem
• Check topic description/literature section for some suggestions
• Explain the main idea
• Highlight benefits and potential shortcomings

• Provide 2-3 points/questions to start the interactive discussion