Despite broad consensus among social scientists that technology embodies values, scholarship is conspicuously lacking in reflexive analysis of the value-based implications of natural language processing (NLP) technologies. To explore embedded values and potential harms in the NLP domain, a systematic literature review grounded in established value frameworks focused on how uninformed design decisions can perpetuate algorithmic biases and lead to discriminatory outcomes. Analysis applying Schwartz's theory of basic values to a sample of 60 articles indexed by the Association for Computing Machinery and Institute of Electrical and Electronics Engineers, published between 2010 and 2023, identified underrepresented values such as stimulation and tradition, and it detected the complete absence of attention to hedonism and benevolence. The omission of scholarly attention to hedonism is particularly surprising, in light of its relevance for social media services; this points to a potential oversight in enhancing personal enjoyment and quality of life. Benevolence's absence indicates unexplored areas of communal application. The study highlights a need for more comprehensive value-based analysis in NLP research, to ensure ethical and inclusive technological development. Highlighting this gap should contribute to cross-discipline dialogue among researchers, technologists, and policymakers, for NLP technologies better aligned with diverse human values.
Robin Forsberg, Matti Nelimarkka· Big Data & Society· 0 citations
Commodity futures are shaped by harvest cycles, weather shocks, storage conditions, and seasonal demand, but it remains unclear whether recurring patterns yield robust out-of-sample trading profits. Existing research documents return seasonality in commodity futures as well as more complex seasonal structure, while leaving less evidence on how alternative seasonal models compare under common implementation constraints. This article compares dummy-variable regression (DVR), Singular Spectrum Analysis (SSA), and robust low-rank SSA (RLSSA) within a unified trading framework, including a volatility-normalised specification. Using monthly delivery-avoidance returns for 15 liquid commodity futures, the models are estimated on rolling ten-year windows and evaluated from 2016 to 2024 with transaction costs, an equal-weight long benchmark, and Maximum Entropy Bootstrap (MEB) assessment. Across 500 MEB paths, the benchmark has the strongest average full-period profile, with a cumulative return of 16.81%, a Sharpe ratio of 0.191, and a maximum drawdown of -0.414. Classical SSA has the strongest average model outcomes, but negative median cumulative returns and deep drawdowns indicate substantial path sensitivity. Volatility normalisation reduces the average DVR short loss but generally weakens SSA-based portfolios. None of the 18 approximate paired MEB Sharpe tests rejects after within-family Holm adjustment. Because MEB preserves each contract's temporal rank ordering, the assessment is conditional on observed timing rather than a timing-randomised seasonal null. The evidence does not establish robust benchmark outperformance and shows that model performance varies materially across market subperiods.
Ralph Kosch, Robin Forsberg· 0 citations
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