Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient framework to mitigate popularity bias from a gradient perspective. Specifically, we first normalize each user embedding and record accumulated gradients of users and items via popularity bias measures in model training. To address the popularity bias issues, we develop a gradient-based embedding adjustment approach used in model testing. This strategy is generic, model-agnostic, and can be seamlessly integrated into most existing recommender systems. Our extensive experiments on two classic recommendation models and four real-world datasets demonstrate the effectiveness of our method over state-of-the-art debiasing baselines.Comment: Recommendation System, Popularity Bia

Should the advanced measurement approach be replaced with the standardized measurement approach for operational risk?

Recently, Basel Committee for Banking Supervision proposed to replace all approaches, including Advanced Measurement Approach (AMA), for operational risk capital with a simple formula referred to as the Standardised Measurement Approach (SMA). This paper discusses and studies the weaknesses and pitfalls of SMA such as instability, risk insensitivity, super-additivity and the implicit relationship between SMA capital model and systemic risk in the banking sector. We also discuss the issues with closely related operational risk Capital-at-Risk (OpCar) Basel Committee proposed model which is the precursor to the SMA. In conclusion, we advocate to maintain the AMA internal model framework and suggest as an alternative a number of standardization recommendations that could be considered to unify internal modelling of operational risk. The findings and views presented in this paper have been discussed with and supported by many OpRisk practitioners and academics in Australia, Europe, UK and USA, and recently at OpRisk Europe 2016 conference in London

New accurate, explainable, and unbiased machine learning models for recommendation with implicit feedback.

Recommender systems have become ubiquitous Artificial Intelligence (AI) tools that play an important role in filtering online information in our daily lives. Whether we are shopping, browsing movies, or listening to music online, AI recommender systems are working behind the scene to provide us with curated and personalized content, that has been predicted to be relevant to our interest. The increasing prevalence of recommender systems has challenged researchers to develop powerful algorithms that can deliver recommendations with increasing accuracy. In addition to the predictive accuracy of recommender systems, recent research has also started paying attention to their fairness, in particular with regard to the bias and transparency of their predictions. This dissertation contributes to advancing the state of the art in fairness in AI by proposing new Machine Learning models and algorithms that aim to improve the user\u27s experience when receiving recommendations, with a focus that is positioned at the nexus of three objectives, namely accuracy, transparency, and unbiasedness of the predictions. In our research, we focus on state-of-the-art Collaborative Filtering (CF) recommendation approaches trained on implicit feedback data. More specifically, we address the limitations of two established deep learning approaches in two distinct recommendation settings, namely recommendation with user profiles and sequential recommendation. First, we focus on a state of the art pairwise ranking model, namely Bayesian Personalized Ranking (BPR), which has been found to outperform pointwise models in predictive accuracy in the recommendation with the user profiles setting. Specifically, we address two limitations of BPR: (1) BPR is a black box model that does not explain its outputs, thus limiting the user\u27s trust in the recommendations, and the analyst\u27s ability to scrutinize a model\u27s outputs; and (2) BPR is vulnerable to exposure bias due to the data being Missing Not At Random (MNAR). This exposure bias usually translates into an unfairness against the least popular items because they risk being under-exposed by the recommender system. We propose a novel explainable loss function and a corresponding model called Explainable Bayesian Personalized Ranking (EBPR) that generates recommendations along with item-based explanations. Then, we theoretically quantify the additional exposure bias resulting from the explainability, and use it as a basis to propose an unbiased estimator for the ideal EBPR loss. This being done, we perform an empirical study on three real-world benchmarking datasets that demonstrate the advantages of our proposed models, compared to existing state of the art techniques. Next, we shift our attention to sequential recommendation systems and focus on modeling and mitigating exposure bias in BERT4Rec, which is a state-of-the-art recommendation approach based on bidirectional transformers. The bi-directional representation capacity in BERT4Rec is based on the Cloze task, a.k.a. Masked Language Model, which consists of predicting randomly masked items within the sequence, assuming that the true interacted item is the most relevant one. This results in an exposure bias, where non-interacted items with low exposure propensities are assumed to be irrelevant. Thus far, the most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning. We first argue and prove that IPS does not extend to sequential recommendation because it fails to account for the sequential nature of the problem. We then propose a novel propensity scoring mechanism, that we name Inverse Temporal Propensity Scoring (ITPS), which is used to theoretically debias the Cloze task in sequential recommendation. We also rely on the ITPS framework to propose a bidirectional transformer-based model called ITPS-BERT4Rec. Finally, we empirically demonstrate the debiasing capabilities of our proposed approach and its robustness to the severity of exposure bias. Our proposed explainable approach in recommendation with user profiles, EBPR, showed an increase in ranking accuracy of about 4% and an increase in explainability of about 7% over the baseline BPR model when performing experiments on real-world recommendation datasets. Moreover, experiments on a real-world unbiased dataset demonstrated the importance of coupling explainability and exposure debiasing in capturing the true preferences of the user with a significant improvement of 1% over the baseline unbiased model UBPR. Furthermore, coupling explainability with exposure debiasing was also empirically proven to mitigate popularity bias with an improvement in popularity debiasing metrics of over 10% on three real-world recommendation tasks over the unbiased UBPR model. These results demonstrate the viability of our proposed approaches in recommendation with user profiles and their capacity to improve the user\u27s experience in recommendation by better capturing and modeling their true preferences, improving the explainability of the recommendations, and presenting them with more diverse recommendations that span a larger portion of the item catalog. On the other hand, our proposed approach in sequential recommendation ITPS-BERT4Rec has demonstrated a significant increase of 1% in terms of modeling the true preferences of the user in a semi-synthetic setting over the state-of-the-art sequential recommendation model BERT4Rec while also being unbiased in terms of exposure. Similarly, ITPS-BERT4Rec showed an average increase of 8.7% over BERT4Rec in three real-world recommendation settings. Moreover, empirical experiments demonstrated the robustness of our proposed ITPS-BERT4Rec model to increasing levels of exposure bias and its stability in terms of variance. Furthermore, experiments on popularity debiasing showed a significant advantage of our proposed ITPS-BERT4Rec model for both the short and long term sequences. Finally, ITPS-BERT4Rec showed respective improvements of around 60%, 470%, and 150% over vanilla BERT4Rec in capturing the temporal dependencies between the items within the sequences of interactions for three different evaluation metrics. These results demonstrate the potential of our proposed unbiased estimator to improve the user experience in the context of sequential recommendation by presenting them with more accurate and diverse recommendations that better match their true preferences and the sequential dependencies between the recommended items

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Accident denominators relative to age groups in heavy industries of the Port Hedland region of Western Australia

The aim of this research is to investigate characteristics of accident denominators across age groups in mining and associated process industries in the Port Hedland region of Western Australia. Emphasis has been focussed on comparing young, inexperienced groups with older, more experienced groups. A literature review revealed some key contributors to accidents among younger workers, in particular, those who had only recently entered the workforce. The review also revealed contributors impacting accidents regarding other age groups over a wide range of industry types. From these findings an accident construct model and questionnaire were designed to identify contributing and mitigating denominators which input to accidents occurring across the defined age groups

Algorithmic Risk Assessments and the Double-Edged Sword of Youth

Risk assessment algorithms—statistical formulas that predict the likelihood a person will commit crime in the future—are used across the country to help make life-altering decisions in the criminal process, including setting bail, determining sentences, selecting probation conditions, and deciding parole. Yet many of these instruments are “black-box” tools. The algorithms they use are secret, both to the sentencing authorities who rely on them and to the offender whose life is affected. The opaque nature of these tools raises numerous legal and ethical concerns. In this paper we argue that risk assessment algorithms obfuscate how certain factors, usually considered mitigating by sentencing authorities, can lead to higher risk scores and thus inappropriately inflate sentences. We illustrate this phenomenon through one of its most dramatic manifestations: The role of age in risk assessment algorithms. When considered as a factor at sentencing, youthfulness can be a double-edged sword—it can both enhance risk and diminish blameworthiness. If either risk or culpability is the sole issue at sentencing, this potential conflict is avoided. But when, as is often the case, both risk and culpability are considered relevant to the sentence, the aggravating effect of youth should presumably be offset or perhaps eliminated entirely by its mitigating impact. If judges and parole authorities are fully informed of the conflicting roles youth plays in a particular case, they can engage in this balancing act as appropriate. However, when information about risk comes from a black-box algorithm, they are unlikely to know the extent to which the risk evaluation is influenced by the defendant’s youthfulness. In such cases, their decisions about pretrial detention, sentence, or release may unknowingly give youth too much weight as an aggravator. Further, even if the black box is opened and the risk assessment algorithm is made publicly available, the risk score may not be conveyed in a fully transparent manner. For instance, while judges may be told that an offender’s youth is a risk factor, the relative weight of age in the overall score may not be fully explained or understood at the time of decision-making. Unless the judge makes specific inquiries, she will not be informed of the variables that contributed most heavily to a particular defendant’s risk score. This decisional blindness is especially pernicious in light of the impression created by the labels associated with these instruments—“high risk” or “high risk of violence.” Such labels not only convey information about the potential for recidivism. They are also suggestive of bad character, or at least a history of bad decision-making. In other words, these labels convey condemnation. Such condemnation might be appropriate for an individual who has earned the “high-risk” classification by committing multiple violent or ruthless acts. But it is not warranted for an individual who has earned that label largely because of his or her youth. To ensure sentencers take this double-edged sword problem into account, risk assessment algorithms should be transparent about the factors that most heavily influence the score. Only in that way can courts and legislators engage in an explicit discussion about whether, and to what extent, young age should be considered a mitigator or an aggravator in fashioning criminal punishment. In Part I, we discuss the tensions youthfulness generates in the post-conviction setting by introducing the double-edge sword phenomenon and the jurisprudence that has developed around it. In Part II, we present empirical evidence that shows how influential age is in the widely-used COMPAS Violent Recidivism Risk Score (VRRS) and in other common risk assessment tools. Specifically, we conduct a partial decomposition of the VRRS to show that age alone can explain almost 60% of its variation, substantially more than the contributions of criminal history, gender or race. Similar patterns are documented in other common risk scores. In Part III, we discuss how obfuscation of age’s impact on the risk score improperly undermines consideration of youthfulness as a mitigating factor. We also discuss how the points we make about the role of youth might apply to a number of other factors that are often used in structured risk assessments, including mental illness, substance abuse, and socio-economic factors. While our discussion centers on sentencing, the main argument is generally relevant to a broad range of settings in which risk assessments influence criminal justice outcomes