Toward an Ethics of AI Belief

Philosophical research in AI has hitherto largely focused on the ethics of AI. In this paper we, an ethicist of belief and a machine learning scientist, suggest that we need to pursue a novel area of philosophical research in AI - the epistemology of AI, and in particular an ethics of belief for AI, i.e., an ethics of AI belief. Here we take the ethics of belief, a field that has been defined in various ways, to refer to a sub-field within epistemology. This subfield is concerned with the study of possible moral, practical, and other non-alethic dimensions of belief. And in this paper, we will primarily be concerned with the normative question within the ethics of belief of what agents - both human and artificial - ought to believe, rather than with descriptive questions concerning whether certain beliefs meet various evaluative standards such as being true, being justified or warranted, constituting knowledge, and so on. We suggest four topics in extant work in the ethics of (human) belief that can be applied to an ethics of AI belief: doxastic wronging by AI; morally owed beliefs; pragmatic and moral encroachment on AI beliefs; and moral responsibility for AI beliefs. We also indicate an important nascent area of philosophical research in epistemic injustice and AI that has not yet been recognized as research in the ethics of AI belief, but which is so in virtue of concerning moral and practical dimensions of belief

Impaired reinforcement learning and Bayesian inference in psychiatric disorders: from maladaptive decision making to psychosis in schizophrenia

Computational modelling has been gaining an increasing amount of support from the neuroscience community as a tool to assay cognition and computational processes in the brain. Lately, scientists have started to apply computational methods from neuroscience to the study of psychiatry to gain further insight into the mechanisms leading to mental disorders. In fact, only recently has psychiatry started to move away from categorising illnesses using behavioural symptoms in an attempt for a more biologically driven diagnosis. To date, several neurobiological anomalies have been found in schizophrenia and led to a multitude of conceptual framework attempting to link the biology to the patients’ symptoms. Computational modelling can be applied to formalise these conceptual frameworks in an effort to test the validity or likelihood of each hypothesis. Recently, a novel conceptual model has been proposed to describe how positive symptoms (delusions, hallucinations and thought disorder) and cognitive symptoms (poor decision-making, i.e. “executive functioning”) might arise in schizophrenia. This framework however, has not been tested experimentally or against computational models. The focus of this thesis was to use a combination of behavioural experiments and computational models to independently assess the validity of each component that make up this framework. The first study of this thesis focused on the computational analysis of a disrupted prediction-error signalling and its implications for decision-making performances in complex tasks. Briefly, we used a reinforcement-learning model of a gambling task in rodents and disrupted the prediction-error signal known to be critical for learning. We found that this disruption can account for poor performances in decision-making due to an incorrect acquisition of the model of the world. This study illustrates how disruptions in prediction-error signalling (known to be present in schizophrenia) can lead to the acquisition of an incorrect world model which can lead to poor executive functioning or false beliefs (delusions) as seen in patients. The second study presented in this thesis addressed spatial working memory performances in chronic schizophrenia, bipolar disorder, first episode psychosis and family relatives of DISC1 translocation carriers. We build a probabilistic inference model to solve the working memory task optimally and then implemented various alterations of this model to test commonly debated hypotheses of cognitive deficiency in schizophrenia. Our goal was to find which of these hypotheses accounts best for the poor performance observed in patients. We found that while the performance at the task was significantly different for most patients groups in comparison to controls, this effect disappeared after controlling for IQ in one group. The models were nonetheless fitted to the experimental data and suggest that working memory maintenance is most likely to account for the poor performances observed in patients. We propose that the maintenance of information in working memory might have indirect implications for measures of general cognitive performance, as these rely on a correct filtering of information against distractions and cortical noise. Finally the third study presented in this thesis assessed the performance of medicated chronic schizophrenia patients in a statistical learning task of visual stimuli and measured how the acquired statistics influenced their perception. We find that patient with chronic schizophrenia appear to be unimpaired at statistical learning of visual stimuli. The acquired statistics however appear to induce less expectation-driven ‘hallucinations’ of the stimuli in the patients group than in controls. We find that this is in line with previous literature showing that patients are less susceptible to expectation-driven illusions than controls. This study highlights however the idea that perceptual processes during sensory integration diverge from this of healthy controls. In conclusion, this thesis suggests that impairments in reinforcement learning and Bayesian inference appear to be able to account for the positive and cognitive symptoms observed in schizophrenia, but that further work is required to merge these findings. Specifically, while our studies addressed individual components such as associative learning, working memory, implicit learning & perceptual inference, we cannot conclude that deficits of reinforcement learning and Bayesian inference can collectively account for symptoms in schizophrenia. We argue however that the studies presented in this thesis provided evidence that impairments of reinforcement learning and Bayesian inference are compatible with the emergence of positive and cognitive symptoms in schizophrenia

Recommendations for Bayesian hierarchical model specifications for case-control studies in mental health

Hierarchical model fitting has become commonplace for case-control studies of cognition and behaviour in mental health. However, these techniques require us to formalise assumptions about the data-generating process at the group level, which may not be known. Specifically, researchers typically must choose whether to assume all subjects are drawn from a common population, or to model them as deriving from separate populations. These assumptions have profound implications for computational psychiatry, as they affect the resulting inference (latent parameter recovery) and may conflate or mask true group-level differences. To test these assumptions we ran systematic simulations on synthetic multi-group behavioural data from a commonly used multi-armed bandit task (reinforcement learning task). We then examined recovery of group differences in latent parameter space under the two commonly used generative modelling assumptions: (1) modelling groups under a common shared group-level prior (assuming all participants are generated from a common distribution, and are likely to share common characteristics); (2) modelling separate groups based on symptomatology or diagnostic labels, resulting in separate group-level priors. We evaluated the robustness of these approaches to variations in data quality and prior specifications on a variety of metrics. We found that fitting groups separately (assumptions 2), provided the most accurate and robust inference across all conditions. Our results suggest that when dealing with data from multiple clinical groups, researchers should analyse patient and control groups separately as it provides the most accurate and robust recovery of the parameters of interest.Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstrac

The flavin reductase ActVB from Streptomyces coelicolor: characterization of the electron transferase activity of the flavoprotein form.

International audienceThe flavin reductase ActVB is involved in the last step of actinorhodin biosynthesis in Streptomyces coelicolor. Although ActVB can be isolated with some FMN bound, this form was not involved in the flavin reductase activity. By studying the ferric reductase activity of ActVB, we show that its FMN-bound form exhibits a proper enzymatic activity of reduction of iron complexes by NADH. This shows that ActVB active site exhibits a dual property with regard to the FMN. It can use it as a substrate that goes in and off the active site or as a cofactor to provide an electron transferase activity to the polypeptide

A two-component flavin-dependent monooxygenase involved in actinorhodin biosynthesis in Streptomyces coelicolor.

International audienceThe two-component flavin-dependent monooxygenases belong to an emerging class of enzymes involved in oxidation reactions in a number of metabolic and biosynthetic pathways in microorganisms. One component is a NAD(P)H:flavin oxidoreductase, which provides a reduced flavin to the second component, the proper monooxygenase. There, the reduced flavin activates molecular oxygen for substrate oxidation. Here, we study the flavin reductase ActVB and ActVA-ORF5 gene product, both reported to be involved in the last step of biosynthesis of the natural antibiotic actinorhodin in Streptomyces coelicolor. For the first time we show that ActVA-ORF5 is a FMN-dependent monooxygenase that together with the help of the flavin reductase ActVB catalyzes the oxidation reaction. The mechanism of the transfer of reduced FMN between ActVB and ActVA-ORF5 has been investigated. Dissociation constant values for oxidized and reduced flavin (FMNox and FMNred) with regard to ActVB and ActVA-ORF5 have been determined. The data clearly demonstrate a thermodynamic transfer of FMNred from ActVB to ActVA-ORF5 without involving a particular interaction between the two protein components. In full agreement with these data, we propose a reaction mechanism in which FMNox binds to ActVB, where it is reduced, and the resulting FMNred moves to ActVA-ORF5, where it reacts with O2 to generate a flavinperoxide intermediate. A direct spectroscopic evidence for the formation of such species within ActVA-ORF5 is reported

Intermolecular electron transfer in two-iron superoxide reductase: a putative role for the desulforedoxin center as an electron donor to the iron active site.

International audienceSuperoxide reductase (SOR) is a superoxide detoxification system present in some microorganisms. Its active site consists of an unusual mononuclear iron center with an FeN4S1 coordination which catalyzes the one-electron reduction of superoxide to form hydrogen peroxide. Different classes of SORs have been described depending on the presence of an additional rubredoxin-like, desulforedoxin iron center, whose function has remained unknown until now. In this work, we investigated the mechanism of the reduction of the SOR iron active site using the NADPH:flavodoxin oxidoreductase from Escherichia coli, which was previously shown to efficiently transfer electrons to the Desulfoarculus baarsii SOR. When present, the additional rubredoxin-like iron center could function as an electronic relay between cellular reductases and the iron active site for superoxide reduction. This electron transfer was mainly intermolecular, between the rubredoxin-like iron center of one SOR and the iron active site of another SOR. These data provide the first experimental evidence for a possible role of the rubredoxin-like iron center in the superoxide detoxifying activity of SOR

Acquisition of visual priors and induced hallucinations in chronic schizophrenia

Prominent theories suggest that symptoms of schizophrenia stem from learning deficiencies resulting in distorted internal models of the world. To test these theories further, we used a visual statistical learning task known to induce rapid implicit learning of the stimulus statistics. In this task, participants are presented with a field of coherently moving dots and are asked to report the presented direction of the dots (estimation task), and whether they saw any dots or not (detection task). Two of the directions were more frequently presented than the others. In controls, the implicit acquisition of the stimuli statistics influences their perception in two ways: (i) motion directions are perceived as being more similar to the most frequently presented directions than they really are (estimation biases); and (ii) in the absence of stimuli, participants sometimes report perceiving the most frequently presented directions (a form of hallucinations). Such behaviour is consistent with probabilistic inference, i.e. combining learnt perceptual priors with sensory evidence. We investigated whether patients with chronic, stable, treated schizophrenia (n = 20) differ from controls (n = 23) in the acquisition of the perceptual priors and/or their influence on perception. We found that although patients were slower than controls, they showed comparable acquisition of perceptual priors, approximating the stimulus statistics. This suggests that patients have no statistical learning deficits in our task. This may reflect our patients’ relative wellbeing on antipsychotic medication. Intriguingly, however, patients experienced significantly fewer (P = 0.016) hallucinations of the most frequently presented directions than controls when the stimulus was absent or when it was very weak (prior-based lapse estimations). This suggests that prior expectations had less influence on patients’ perception than on controls when stimuli were absent or below perceptual threshold