Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations

Deep Neural Network (DNN) models are challenging to interpret because of their highly complex and non-linear nature. This lack of interpretability (1) inhibits adoption within safety critical applications, (2) makes it challenging to debug existing models, and (3) prevents us from extracting valuable knowledge. Explainable AI (XAI) research aims to increase the transparency of DNN model behaviour to improve interpretability. Feature importance explanations are the most popular interpretability approaches. They show the importance of each input feature (e.g., pixel, patch, word vector) to the model’s prediction. However, we hypothesise that feature importance explanations have two main shortcomings concerning their inability to describe the complexity of a DNN behaviour with sufficient (1) fidelity and (2) richness. Fidelity and richness are essential because different tasks, users, and data types require specific levels of trust and understanding. The goal of this thesis is to showcase the shortcomings of feature importance explanations and to develop explanation techniques that describe the DNN behaviour with greater richness. We design an adversarial explanation attack to highlight the infidelity and inadequacy of feature importance explanations. Our attack modifies the parameters of a pre-trained model. It uses fairness as a proxy measure for the fidelity of an explanation method to demonstrate that the apparent importance of a feature does not reveal anything reliable about the fairness of a model. Hence, regulators or auditors should not rely on feature importance explanations to measure or enforce standards of fairness. As one solution, we formulate five different levels of the semantic richness of explanations to evaluate explanations and propose two function decomposition frameworks (DGINN and CME) to extract explanations from DNNs at a semantically higher level than feature importance explanations. Concept-based approaches provide explanations in terms of atomic human-understandable units (e.g., wheel or door) rather than individual raw features (e.g., pixels or characters). Our function decomposition frameworks can extract specific class representations from 5% of the network parameters and concept representations with an average-per-concept F1 score of 86%. Finally, the CME framework makes it possible to compare concept-based explanations, contributing to the scientific rigour of evaluating interpretability methods.The author would like to appreciate the generous sponsorship of the Engineering and Physical Sciences Research Council (EPSRC), The Department of Computer Science and Technology at the University of Cambridge, and Tenyks, Inc

Rationality in Artificial Intelligence Decision-making

Organisaatioiden päätöksenteossa käytetään enenevissä määrin tekoälyä, jonka odotetaan luovan kilpailuetua sitä käyttäville. Kuitenkin uusien mahdollisuuksien ja hyötyjen myötä päädytään myös uusien ongelmien ja haasteiden pariin. Tekoälyn osalta merkittävä osa näistä haasteista koskee rationaliteettia, jolla tässä tarkoitetaan päätöksenteon takana olevia syitä, niiden suhteita toisiinsa, sekä prosessia, jonka tuloksena ne saadaan. Tekoälyn luomat haasteet päästä näkemään ja ymmärtämään päätösten takana olevia rationaliteetteja luo huolta päätöksenteon reiluudesta, vastuusta, ja luottamuksesta päätöksentekoprosessiin. Lisäksi tekoälyn käyttämän rationaliteetin katsotaan luovan haasteita moraaliselle, refleksiiviselle harkintakyvylle päätöksenteossa. Rationaliteetti sekä toimijuus ovat molemmat oleellisia päätöksenteon kannalta, mutta toimijuus on käsitteenä kehittynyt suuntaan, jossa teknologia ja ihminen nähdään erottamattomia toimijuuden suhteen. Niiden katsotaan muodostavan yhdessä yhteinen toimijuus. Tekoälykeskusteluissa rationaaliteettiin sen sijaan on juurtunut syvälle dualistinen ajattelu, joka on toimijuuden suhteen jo hylätty. Dualistisen ajattelutavan rationaliteetin suhteen voidaan katsoa ylläpitävän tunnistettuja ongelmia tekoälyn suhteen. Tekoälyn rationaliteetin laatua on käsitelty teoreettisesti, mutta tutkimuskentältä puuttuu vielä empiirinen tutkimus aiheesta. Tämä väitöskirja käyttää postfenomenologiaa empiiriseen tutkimukseen siitä, miten tekoälyn käyttö muuttaa päätöksenteon rationaliteettia. Postfenomenologia on yhteensopiva toimijuuden kanssa, joka ymmärretään ei-dualistiseksi. Sen sijaan post- fenomenologia käsittää teknologian “välittäjänä” ihmisten toimijuudelle. Tämä väitöskirja käyttää vastaavaa näkemystä rationaliteetin tarkasteluun, ja siten tuo rationaliteetin ei-dualistisen tarkastelun tasa-arvoiseksi toimijuuden kanssa päätöksenteossa. Esitetty tutkimuskysymys on “Kuinka tekoäly toimii välittäjänä rationaliteetille päätöksenteossa?” Postfenomenologinen analyysi on tarkoitettu käytettäväksi kun tutkitaan tiettyjä teknologioita ja sitä, miten ne toimivat välittäjinä ihmisten olemiselle ja kokemuksille. Nämä välitykset voidaan jakaa ulottuvuuksiin, jotka tässä väitöskirjassa ovat piilottaminen–paljastaminen, mahdollistava–rajoittava, sekä vieraannuttava– osallistava. Empiiriset tutkimukset luovat postfenomenologiassa perustan filosofiselle ja konseptuaaliselle analyysille. Tyypillisesti nämä ovat tapaustutkimuksia konkreettisista teknologioista, jotka voivat olla primäärisiä omia tutkimuksia, perustua sekundääriseen materiaaliin, tai olla tutkijan omaa reflektiota. Vaikka väitöskirjan julkaisut eivät itsessään ole olleet tapaustutkimuksia, käytetty postfenomenologinen tutkimusote käsittää ne sellaisina muodostaen väitöskirjasta monitapaustutkimuksen. Neljä ensimmäistä julkaisua ovat empiirisiä tekoälysovelluksia erilaisilla, mutta verrattavissa olevilla datoilla ja tutkimusasetelmilla. Viimeinen julkaisu on teoreettinen, ja se täydentää aiempia julkaisuita tarjoamalla näkökulman tarkasteltavaan vieraannuttava– osallistava-ulottuvuuteen. Tekoälyn havaittiin piilottavan päätöksien rationaliteettia useissa eri päätöksentekoprosessin vaiheissa, mutta toisaalta myös paljastavan tiettyjä uusia rationaliteettimahdollisuuksia. Piilotuksesta löydettiin kaksi eri tasoa. Ensimmäisellä tasolla rationaliteetin sisältö on piilossa, mutta on nähtävissä, että jotain rationaliteettia on käytetty. Toisella tasolla on piilossa, että päätökseen on edes käytetty rationaliteettia. Sen sijaan päätös vaikuttaa tapahtuneen ilman syitä ikää kuin “automaattisesti.” Rationaliteeteista muodostui abstraktimpeja ja jäykempiä riippumatta tekoälyn käytöstä päätöksenteossa, mikä kuitenkin tyypillisesti paljasti rationaliteetin sisältöä kun päätöksenteko oli osallistavaa, kun taas vieraantuneessa päätöksenteossa tämä prosessi ja rationaliteetti jäi piiloon. Tekoäly luonteensa vuoksi rajoitti rationaliteetteja vertailemaan datan erilaisuuksia ja samanlaisuuksia. Tulokset vihjaavat, että ihmiset ovat itse osallistuvat omaan vieraantumiseensa päätöksenteossa tekoälyn kanssa erityisesti rationaliteetin piilottamisen kautta. Tämä väitöskirja tarjoaa uusia näkemyksiä ja tarkemman tarkastelutason rationaliteettiin ja sen moraaliin tekoälyavusteisessa päätöksenteossa. Väitöskirja myös tarjoaa testattavia väitteitä tekoälyn välityksistä, joita voidaan käyttää teorian kehittämiseen tekoälyn reiluuden ja vastuun näkökulmista. Lisäksi väitöskirja vie rationaliteetin ja organisaatioiden päätöksenteon tutkimuskenttää eteenpäin jättämällä tarpeettoman dualismin pois rationaliteetin osalta. Löydökset myös auttavat ammattilaisia löytämään oleellisia tekoälyn vaikutuksia, jotka on syytä huomioida onnistuneen tekoälyn käytön kannalta.Artificial intelligence (AI) has become increasingly ubiquitous in a variety of organizations for decision-making, and it promises competitive advantages to those who use it. However, with the novel insights and benefits of AI come unprecedented side-effects and externalities, which circle around a theme of rationality. A rationality for a decision is the reasons, the relationships between the reasons, and the process of their emergence. Lack of access to the decision rationality of AI is posed to cause issues with trust in AI due to lack of fairness and accountability. Moreover, AI rationality in moral decisions is seen to pose threats to reflective moral capabilities. While rationality and agency are both fundamental to decision-making, agency has seen a shift into more relational views in which the technical and social are seen as inseparable and co-constituting of each other. However, AI rationality discussions are still heavily entrenched in dualism that has been overcome regarding agency. This entrenchment can contribute to a variety of the issues noted around AI. Moreover, while the types of AI rationality have been considered theoretically, currently the field lacks empirical work to support the discussions revolving around AI rationality. This dissertation uses postphenomenology as a methodology to study empiri- cally how AI in decision-making impacts rationality. Postphenomenology honours anti-dualistic agency: Technology mediates and co-constitutes agency with people in intra-action. This dissertation uses this approach to study the mediation of rationality. Thus, it helps views on rationality to catch up with agency in terms of overcoming unnecessary dualism. The posed research question is “How does AI mediate rationality in decision-making?” Postphenomenological analysis is meant to be used at the level of the technological mediations of a specific technology, such as AI mediation of rationality in decision-making. Mediations can be considered in dimensions. This dissertation considers revealing–concealing, enabling–constraining, and involving–alienating dimensions of mediation to answer the posed research question. In postphenomenology a basis for analysis is provided by empirical works, which are typically case studies of concrete intra-actions between humans and technologies. Postphenomenology as a methodology allows secondary empirical work by others, primary self-conducted studies, and first-person reflection as basis for empirical case analysis. Thus, while the publications of this dissertation are not published as case studies, postphenomenology considers them as such, making this dissertation a multiple case study. The first four publications are empirical works of applied AI with various different types of combinations of human and AI decision-making tasks with different yet comparable data. Data and methodology remain similar across studies in the empirical publications and are well comparable for postphenomenological analysis as case studies. The last publication is a theoretical paper, which provides a complement to the empirical publications on the involving–alienating dimension. AI was found to conceal decision rationality in various stages of AI decision making, while in some cases AI also revealed possibilities for specific, novel rationalities. Two levels of rationality concealment were discovered: The contents of a rationality could become concealed, but also the presence of a rationality in the first place could become concealed. Rationality became more abstract and formalized regardless of whether the rationality was constructed with an AI or not. This formalization constrained rationality by ruling out other valid rationalities. Constraint also happened due to rationalities necessarily taking the specific form of similarities versus differences in the data. The results suggest that people can become involved in their alienation from rationality in AI decision-making. Study of the relationships between the mediation dimensions suggest that the constraint of formalization was revealing with involvement. Otherwise, formalization was both concealed because of and resulted in alienation from AI in decision-making. Results point to the direction that people may be involved in their own alienation via rationality concealment. This dissertation contributes new insights and levels of analysis for AI rationality in decision-making and its moral implications. It provides testable claims about technological mediations that can be used to develop theory and posits that they can be useful in theorizing how to increase AI fairness, accountability, and transparency. Moreover, the dissertation contributes to the field of rationality in management and organizational decision-making by developing rationality beyond unnecessary dualism. For practitioners, the findings guide them to identify relevant AI mediations in decision-making to consider to ensure successful AI adoption and mitigation of its issues in their specific contexts

Assessing deep learning : a work program for the humanities in the age of artificial intelligence

How to read this paper: It is structured in four modular parts: a general introduction (section 1), an introduction to the workings of DL for uninitiated non-technical readers (section 2), a more mathematical introduction to DL (appendix A), and a main part, containing the outlines of a work program for the humanities (section 3). Readers familiar with mathematical notions might want to skip 2 and instead read A. Readers familiar with DL in general might want to ignore 2 and A altogether and instead directly read 3 after 1.Following the success of deep learning (DL) in research, we are now witnessing the fast and widespread adoption of arti cial intelligence (AI) in daily life, influencing the way we act, think, and organize our lives. However, much still remains a mystery when it comes to how these systems achieve such high performance and why they reach the outputs they do. This presents us with an unusual combination: of technical mastery on the one hand, and a striking degree of mystery on the other. This conjunction is not only fascinating, but it also poses considerable risks, which urgently require our attention. Awareness of the need to analyze ethical implications, such as fairness, equality, and sustainability, is growing. However, other dimensions of inquiry receive less attention, including the subtle but pervasive ways in which our dealings with AI shape our way of living and thinking, transforming our culture and human self-understanding. If we want to deploy AI positively in the long term, a broader and more holistic assessment of the technology is vital, involving not only scientic and technical perspectives but also those from the humanities. To this end, we present outlines of a work program for the humanities that aim to contribute to assessing and guiding the potential, opportunities, and risks of further developing and deploying DL systems

Collective Bargaining and the Gig Economy

This open access book investigates the role of collective bargaining in the gig economy. Despite the variety of situations covered by the term “gig economy”, collective agreements for employees and non-employees are being concluded in various countries, either at company or at branch level. Offline workers such as riders, food deliverers, drivers or providers of cleaning services are slowly gaining access to the series of negotiated rights that, in the past, were only available to employees. The chapters analyse recent high-profile decisions including Uber in France’s Court de Cassation, Glovo in the Spanish Supreme Court, and Uber in the UK Supreme Court. They evaluate the bargaining agents in different Member States of the EU, to determine whether established actors are participating in the dynamics of the gig economy or if they are being substituted, totally or partially, by new agents. Interesting best practices are drawn from the comparison, also as regards the contents of collective bargaining, raising awareness in those countries that are being left behind in the dynamics of the gig economy. The book collects the results of the COGENS (VS/2019/0084) research project, funded by the European Union, that gathered scholars and stakeholders from 17 countries. It will be an invaluable resource for scholars, trade unionists and policy makers. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com

Collective Bargaining and the Gig Economy

This open access book investigates the role of collective bargaining in the gig economy. Despite the variety of situations covered by the term “gig economy”, collective agreements for employees and non-employees are being concluded in various countries, either at company or at branch level. Offline workers such as riders, food deliverers, drivers or providers of cleaning services are slowly gaining access to the series of negotiated rights that, in the past, were only available to employees. The chapters analyse recent high-profile decisions including Uber in France’s Court de Cassation, Glovo in the Spanish Supreme Court, and Uber in the UK Supreme Court. They evaluate the bargaining agents in different Member States of the EU, to determine whether established actors are participating in the dynamics of the gig economy or if they are being substituted, totally or partially, by new agents. Interesting best practices are drawn from the comparison, also as regards the contents of collective bargaining, raising awareness in those countries that are being left behind in the dynamics of the gig economy. The book collects the results of the COGENS (VS/2019/0084) research project, funded by the European Union, that gathered scholars and stakeholders from 17 countries. It will be an invaluable resource for scholars, trade unionists and policy makers. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com

The Role of Linguistics in Probing Task Design

Over the past decades natural language processing has evolved from a niche research area into a fast-paced and multi-faceted discipline that attracts thousands of contributions from academia and industry and feeds into real-world applications. Despite the recent successes, natural language processing models still struggle to generalize across domains, suffer from biases and lack transparency. Aiming to get a better understanding of how and why modern NLP systems make their predictions for complex end tasks, a line of research in probing attempts to interpret the behavior of NLP models using basic probing tasks. Linguistic corpora are a natural source of such tasks, and linguistic phenomena like part of speech, syntax and role semantics are often used in probing studies. The goal of probing is to find out what information can be easily extracted from a pre-trained NLP model or representation. To ensure that the information is extracted from the NLP model and not learned during the probing study itself, probing models are kept as simple and transparent as possible, exposing and augmenting conceptual inconsistencies between NLP models and linguistic resources. In this thesis we investigate how linguistic conceptualization can affect probing models, setups and results. In Chapter 2 we investigate the gap between the targets of classical type-level word embedding models like word2vec, and the items of lexical resources and similarity benchmarks. We show that the lack of conceptual alignment between word embedding vocabularies and lexical resources penalizes the word embedding models in both benchmark-based and our novel resource-based evaluation scenario. We demonstrate that simple preprocessing techniques like lemmatization and POS tagging can partially mitigate the issue, leading to a better match between word embeddings and lexicons. Linguistics often has more than one way of describing a certain phenomenon. In Chapter 3 we conduct an extensive study of the effects of lingustic formalism on probing modern pre-trained contextualized encoders like BERT. We use role semantics as an excellent example of a data-rich multi-framework phenomenon. We show that the choice of linguistic formalism can affect the results of probing studies, and deliver additional insights on the impact of dataset size, domain, and task architecture on probing. Apart from mere labeling choices, linguistic theories might differ in the very way of conceptualizing the task. Whereas mainstream NLP has treated semantic roles as a categorical phenomenon, an alternative, prominence-based view opens new opportunities for probing. In Chapter 4 we investigate prominence-based probing models for role semantics, incl. semantic proto-roles and our novel regression-based role probe. Our results indicate that pre-trained language models like BERT might encode argument prominence. Finally, we propose an operationalization of thematic role hierarchy - a widely used linguistic tool to describe syntactic behavior of verbs, and show that thematic role hierarchies can be extracted from text corpora and transfer cross-lingually. The results of our work demonstrate the importance of linguistic conceptualization for probing studies, and highlight the dangers and the opportunities associated with using linguistics as a meta-langauge for NLP model interpretation

From Hesiod to Saussure, from Hippocrates to Jevons: An Introduction to the History of Scientific Thought between Iran and the Atlantic

This work offers an introduction to the history of scientific thought in the region between Iran and the Atlantic from the beginnings of the Bronze Age until 1900 CE—a “science” that can be understood more or less as a German Wissenschaft: a coherent body of knowledge carried by a socially organized group or profession. It thus deals with the social and human as well as medical and natural sciences and, in earlier times, even such topics as astrology and exorcism. It discusses eight periods or knowledge cultures: Ancient Mesopotamia – classical Antiquity – Islamic Middle Ages – Latin Middle Ages – Western Europe 1400–1600 – 17th century – 18th century – 19th century. For each period, a general description of scientific thought is offered, embedded within its social context, together with a number of shorter or longer commented extracts from original works in English translation

State of New Hampshire. Reports, 1907-1908, volume II.- Biennial

Sometimes issued both annually and biennially; Each vol. contains the reports of various departments of the government of the state of New Hampshire; Includes attorneys general\u27s opinion

State of New Hampshire. Reports, 1907-1908, volume II.- Biennial

Sometimes issued both annually and biennially; Each vol. contains the reports of various departments of the government of the state of New Hampshire; Includes attorneys general\u27s opinion