Empowering Users to Detect Data Analytics Discriminatory Recommendations

Notwithstanding the various benefits ascribed to using Data Analytics (DA) tools in support of decision-making, they have been blamed for their potential to generate discriminatory outputs. Although several purely technical methods have been proposed to help with this issue, they have proven to be inadequate. In this research-in-progress paper, we aim to address this gap by helping users detect discrimination, if any, in DA recommendations. By drawing upon the moral intensity literature and the literature on explaining black box models, we propose two decisional guidance mechanisms for DA users: (i) aggregated demographic information about the data subjects (ii) information on the variables that drive the DA output and the extent of their contribution along with information about demographics of the data set being analyzed. We suggest that these mechanisms can help decrease users’ readily acceptance of discriminatory DA recommendations. Moreover, we outline an experimental methodology to test our hypotheses

Interpretable Narrative Explanation for ML Predictors with LP: A Case Study for XAI

In the era of digital revolution, individual lives are going to cross and interconnect ubiquitous online domains and offline reality based on smart technologies\u2014discovering, storing, processing, learning, analysing, and predicting from huge amounts of environment-collected data. Sub-symbolic techniques, such as deep learning, play a key role there, yet they are often built as black boxes, which are not inspectable, interpretable, explainable. New research efforts towards explainable artificial intelligence (XAI) are trying to address those issues, with the final purpose of building understandable, accountable, and trustable AI systems\u2014still, seemingly with a long way to go. Generally speaking, while we fully understand and appreciate the power of sub-symbolic approaches, we believe that symbolic approaches to machine intelligence, once properly combined with sub-symbolic ones, have a critical role to play in order to achieve key properties of XAI such as observability, interpretability, explainability, accountability, and trustability. In this paper we describe an example of integration of symbolic and sub-symbolic techniques. First, we sketch a general framework where symbolic and sub-symbolic approaches could fruitfully combine to produce intelligent behaviour in AI applications. Then, we focus in particular on the goal of building a narrative explanation for ML predictors: to this end, we exploit the logical knowledge obtained translating decision tree predictors into logical programs

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Uncovering exceptional predictions using exploratory analysis of second stage machine learning.

Nowadays, algorithmic systems for making decisions are widely used to facilitate decisions in a variety of fields such as medicine, banking, applying for universities or network security. However, many machine learning algorithms are well-known for their complex mathematical internal workings which turn them into black boxes and makes their decision-making process usually difficult to understand even for experts. In this thesis, we try to develop a methodology to explain why a certain exceptional machine learned decision was made incorrectly by using the interpretability of the decision tree classifier. Our approach can provide insights about potential flaws in feature definition or completeness, as well as potential incorrect training data and outliers. It also promises to help find the stereotypes learned by machine learning algorithms which lead to incorrect predictions and especially, to prevent discrimination in making socially sensitive decisions, such as credit decisions as well as crime-related and policing predictions

A Survey Of Methods For Explaining Black Box Models

In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation. The aim of this paper is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.Comment: This work is currently under review on an international journa

Explaining Machine Learning Models by Generating Counterfactuals

Nowadays, machine learning is being applied in various domains, including safety critical areas, which directly affect our lives. These systems are so complex and rely on huge amounts of training data, so that we risk to create systems that we do not understand, which might lead to undesired behavior, such as fatal decisions, discrimination, ethnic bias, racism and others. Moreover, European Union recently adopted General Data Protection Regulation (GDPR), which requires companies to provide meaningful explanation of the logic behind decisions made by machine learning systems, if these decisions affect directly a human being. We address the issue of explaining various machine-learning models by generating counterfactuals for given data points. Counterfactual is a transformation, which shows how to alternate an input object, so that a classifier predicts a different class. Counterfactuals allow us to better understand why particular classification decisions take place. They may aid in troubleshooting a classifier and identifying biases by looking at alternations needed to be made in the data instances. For example, if a loan approval application system denies a loan for a particular person, and we can find a counterfactual indicating that we need to change the gender, or the race of a person for the loan to be approved, then we have identified bias in the model and we need to study our classifier better and retrain it to avoid such undesired behavior. In this thesis we propose a new framework to generate counterfactuals for a set of data points. The proposed framework aims to find a set of similar transformations to data points, such that those changes significantly reduce the probabilities of the target class. We argue that finding similar transformations for a set of data points helps to achieve more robust explanations to classifiers. We demonstrate our framework on 3 types of data: tabular, images and texts. We evaluate our model on both simple and real-world datasets, including ImageNet and 20 NewsGroups