{"id":"216baf90-4f24-46cc-b49c-352d64ef3a63","arxiv_id":"2411.18393","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Applying self-determination theory to AI development, the paper argues rewards such as bug bounties can foster developers' proactive accountability behavior, while sanctions tend to undermine it.","lead":"Giving AI developers rewards such as bug bounties may encourage them to behave accountably on their own initiative, while sanctions may discourage such behavior. This paper builds a theory-based model to explain why, and proposes testing it experimentally.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The paper's selected reward mechanism (bug bounties) is a tangible, performance-contingent incentive that CET predicts will be experienced as controlling; without evidence that bug bounties are non-pressuring, P1 and P3 may invert.","rationale":"The reader's weakest-assumption analysis identified exactly the same load-bearing concern: the model assumes bug bounties are non-controlling, competence-affirming rewards, whereas CET predicts tangible external rewards can be controlling and undermine intrinsic motivation. I agree with this assessment. The paper is a research-in-progress workshop paper with no empirical test, so the concern is not that the authors have made a false empirical claim, but that their central theoretical claim is conditioned on an unexamined assumption about the reward type. The paper even states that reward type determines impact, but then fails to show that bug bounties satisfy the required condition. This is not a disagreement with the broader SDT/CET consensus; it is an internal consistency issue between the propositions and the chosen operationalization. A scenario-based experiment with validated measures of perceived autonomy and intrinsic motivation would directly test whether the bug-bounty condition behaves as the model predicts or as CET's classic undermining effect predicts. Since the reader already rendered a CONDITIONAL verdict and this concern supports that conditionality, no change to the verdict is needed.","tokens_in":5634,"tokens_out":3113,"duration_ms":30363,"concrete_test":"Conduct the planned scenario-based experiment with at least three conditions: bug-bounty reward, non-tangible informational reward (e.g., positive public feedback for proactive accountability actions), and sanction, plus a no-mechanism control. After the manipulation, measure perceived autonomy support and psychological threat to autonomy (using, e.g., the Perceived Autonomy Support scale from the Work Climate Questionnaire and the Intrinsic Motivation Inventory), alongside reported proactive accountability behavior. If the bug-bounty condition yields significantly lower perceived autonomy or intrinsic motivation than the informational-reward condition, then P3 is contradicted for the paper's chosen reward mechanism, and the central claim would need to be restricted to non-tangible rewards.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that rewards increase—and sanctions decrease—AI developers' intrinsic motivation, and that this motivates proactive AI accountability behavior (P1–P5). Within the paper's own framework (CET), this holds only if the reward is experienced as informational and non-controlling. The paper acknowledges this condition: 'CET stresses that the type of reward or sanction determines their impact' (Preliminary Findings, p. 8). However, the reward mechanism selected for contextualization—bug bounties—is a tangible, performance-contingent monetary incentive. The same meta-analysis the paper cites (Deci et al. 1999) shows that such tangible rewards tend to be perceived as controlling and can undermine intrinsic motivation, particularly for activities that are already interesting. The paper offers no argument or evidence that bug bounties are perceived by AI developers as competence-affirming, non-pressuring feedback rather than as controlling compensation. P1 and P3 explicitly require rewards to increase competence and autonomy through informational, non-pressuring channels; if bug bounties are instead experienced as controlling, the sign of the reward effect inverts, and the model's proposed alternative to sanctions loses its foundation. This is the load-bearing gap: the theoretical model is internally coherent, but its contextualization to bug bounties is unsupported by the theory it uses.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"This research-in-progress paper argues that AI accountability is currently dominated by sanctions, which gives it a negative, reactive connotation. The authors propose a proactive AI accountability behavior concept, defined as employees' self-initiated and future-oriented actions to justify and explain their design, use, or decisions regarding AI-based systems. Drawing on Self-Determination Theory (SDT) and Cognitive Evaluation Theory (CET), the paper develops propositions P1-P5: rewards increase competence and autonomy and thereby intrinsic motivation, sanctions decrease competence and autonomy and thereby intrinsic motivation, and intrinsic motivation induces proactive AI accountability behavior. To contextualize the model, the authors survey AI accountability literature to identify sanctions and cybersecurity literature to identify bug bounties as a promising reward mechanism. The paper concludes by outlining a planned scenario-based experiment with AI developers and lists three expected contributions: conceptualizing proactive AI accountability behavior, showing rewards' potential and sanctions' drawbacks, and contributing to CET.","tokens_in":6004,"tokens_out":3133,"duration_ms":30106,"significance":"If the proposed model holds, the paper would offer a concrete governance alternative to sanction-centric AI accountability and would extend motivation theory to a timely domain. The strengths of the manuscript are its clear grounding in an established theory, its explicit acknowledgement that the type of reward or sanction determines the effect, and its concrete plan for an experimental test. The propositions are imported from external theories and the literature review rather than fitted to the paper's own data, so there is no circular reasoning burden. However, the paper is a research-in-progress piece, and its contribution currently rests on a contextualization step that the manuscript does not yet support, as detailed in the major comments.","major_comments":[{"comment":"P1 and P3 require rewards to be experienced as informational and non-pressuring, yet bug bounties are tangible, performance-contingent monetary incentives. The paper cites Deci et al. (1999), whose meta-analysis finds that such rewards tend to be perceived as controlling and can undermine intrinsic motivation, and the paper itself notes that 'the type of reward or sanction determines their impact.' Without an argument or evidence that AI developers perceive bug bounties as competence-affirming feedback rather than as controlling compensation, the sign of the reward effect in P1 and P3 may invert, and the proposed alternative to sanctions loses its foundation. Please either restrict the propositions to demonstrably informational rewards or provide pilot evidence or manipulation checks that bug bounties satisfy the non-controlling condition.","section":"Preliminary Findings, p. 8 (bug bounties)"},{"comment":"The propositions treat sanctions as uniformly autonomy- and competence-thwarting, but the CET framework the paper adopts is event-type dependent: a sanction delivered as constructive feedback or as redress could carry informational value and support competence, and the paper's own literature review identifies redress (apologies, compensation) as a sanction category. P2 and P4 should either be conditioned on the controlling, punitive subtype or the model should allow sanction type to moderate the effect; otherwise the model overstates what the cited theory implies.","section":"The Impact of Rewards and Sanctions on AI Developers' Competence/Autonomy (P2, P4)"},{"comment":"The new construct 'proactive AI accountability behavior' is defined conceptually but not yet operationalized. Since P5 makes this construct the behavioral outcome of the model and the planned experiment depends on measuring it, the manuscript should include at least a draft measurement approach or should explicitly state that operationalization is part of the planned next steps. Without such an operationalization, P5 is not yet testable and the proposed experiment cannot be evaluated.","section":"Background: The Need for Proactive AI Accountability Behavior, p. 4"}],"minor_comments":[{"comment":"The statement 'We reviewed 24 studies' lacks a description of the search and selection process; a short protocol or a reference list of the reviewed studies would help readers evaluate the representativeness of the identified sanctions and rewards.","section":"Preliminary Findings, p. 8"},{"comment":"The label 'AI accountability rewards' in P1 and P3 is not explicitly tied to the bug bounty mechanism until the Preliminary Findings section; connecting the two in the proposition wording would make the model easier to interpret.","section":"Theoretical Model, P1 and P3"},{"comment":"Figure 1 is described in the text but not displayed in the manuscript; including the figure with numbered paths would clarify which relationships are proposed versus already supported in the literature.","section":"Theoretical Model, Figure 1"},{"comment":"Some references have incomplete bibliographic details; for example, Chowdhury and Williams (2021) gives only a blog URL, and several conference papers lack location or proceedings information. Please align all entries with the target citation style.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"The paper is clearly labeled as research-in-progress, and the theoretical derivation is coherent. The main concern is that the chosen reward mechanism (bug bounties) is in tension with the paper's own theoretical basis, and this tension is central to the paper's proposed alternative to sanctions. This is fixable either by narrowing the theoretical claims or by adding pilot evidence, so I do not see it as a fundamental flaw, but it does require substantive revision before the model can be considered defensible."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"First, the basics: this is a research-in-progress paper that proposes a theoretical model. It argues that rewards increase AI developers' competence and autonomy, and sanctions reduce them, which in turn shapes intrinsic motivation and proactive AI accountability behavior. The genuinely new piece is the construct of 'proactive AI accountability behavior' and the explicit use of SDT/CET to think about rewards as an alternative to sanctions. The paper is clearly written, honest about being untested, and the propositions follow from the theory. That is real value for a workshop paper.\n\nWhat the paper does well: it conceptualizes proactive accountability in a way that connects the accountability-as-virtue literature to proactive work behavior, and it grounds its propositions in the classic SDT/CET findings. The literature review to identify sanctions and bug bounties as concrete mechanisms is a useful contextualization, and the planned scenario experiment is a sensible next step.\n\nThe soft spot, and it is not minor: CET predicts that tangible, performance-contingent rewards can be controlling and undermine intrinsic motivation. Bug bounties are exactly that kind of reward. The paper acknowledges that 'the type of reward or sanction determines their impact' but never argues that AI developers would experience bug bounties as informational and non-pressuring. If bug bounties are experienced as controlling, P1 and P3 flip sign, and the model loses its main practical implication. This is a load-bearing gap, not an omitted control variable. Also, P2 and P4 treat sanctions as uniformly detrimental; CET and the sanctions literature distinguish between controlling pressure and other accountability processes, so the model overgeneralizes here too.\n\nWho this is for: researchers working on AI accountability and organizational governance who want a scaffold for thinking about motivation-based mechanisms. Practitioners should be cautious about adopting the bug bounty recommendation until the theory is resolved. Given the research-in-progress framing, the internal tension is fixable: the authors could specify conditions under which rewards are informational, or pick a reward type that is less obviously controlling.\n\nI would send this to peer review in a workshop or theory-development venue. It deserves a serious referee. For a full archival journal, I'd want the experiment and a much tighter treatment of reward types. Engage with it.","headline":"Clean SDT/CET reasoning for proactive AI accountability, but the chosen reward (bug bounties) may be controlling under the theory it invokes.","tokens_in":6343,"tokens_out":2659,"would_cite":false,"duration_ms":23995,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper argues that rewards, not sanctions, can motivate AI developers to proactively account for their systems, by raising intrinsic motivation through competence and autonomy.","keywords":["AI accountability","proactive behavior","intrinsic motivation","Self-Determination Theory","Cognitive Evaluation Theory","bug bounties","sanctions","AI developers"],"falsifier":"A randomized scenario experiment with AI developers that compares a bug-bounty reward condition, a sanction condition, and a no-intervention control, measuring intrinsic motivation and self-initiated accountability actions; if rewarded developers do not show higher intrinsic motivation and more proactive behavior than the control, the paper's propositions fail.","tokens_in":5447,"feed_emoji":"🏆","tokens_out":6720,"duration_ms":55138,"temperature":0.7,"pith_summary":"The paper sets out to overturn the assumption that AI accountability must center on sanctions, arguing that rewards can foster proactive accountability behavior among AI developers. It builds a theoretical model from Self-Determination Theory and Cognitive Evaluation Theory, with propositions that AI accountability rewards increase developers' competence and autonomy, sanctions lower both, and the resulting intrinsic motivation drives self-initiated, future-oriented accountability actions. Concretely, the paper proposes bug bounties as a reward mechanism well suited to encouraging developers to find and report flaws such as algorithmic bias. The model is presented as preliminary, with propositions to be tested in a scenario-based experiment. If the model holds, organizations would have an alternative to punitive governance that is likely to elicit more willing and persistent accountability behavior.","feed_headline":"Rewards, not sanctions, may unlock AI developer accountability","feed_subtitle":"A theory paper predicts bug bounties boost intrinsic motivation and proactive AI reporting, while penalties backfire.","key_machinery":"The load-bearing mechanism is the need-satisfaction pathway from Self-Determination Theory and its sub-theory Cognitive Evaluation Theory: interpersonal events affect intrinsic motivation through the psychological needs for competence and autonomy. Rewards that convey positive feedback and non-pressuring choice are said to satisfy these needs and raise intrinsic motivation; sanctions and threats are said to thwart them and lower intrinsic motivation. Intrinsic motivation is then the proximate cause of proactive AI accountability behavior. Bug bounties—payments for reporting flaws such as algorithmic bias—are selected as the concrete reward instance because they appear in cybersecurity practice as competence-affirming, voluntary opportunities rather than punishments.","core_discovery":"On its own terms, this paper claims that the way AI accountability is enforced changes developers' motivation, and motivation changes behavior. Rewards such as bug bounties are predicted to function as informational, non-pressuring events that satisfy the psychological needs for competence and autonomy (P1, P3), while sanctions are predicted to act as controlling, competence-diminishing events that thwart those needs (P2, P4). Satisfying these needs raises intrinsic motivation, and increased intrinsic motivation is proposed to produce higher levels of proactive AI accountability behavior—self-initiated actions like documenting decisions, flagging bias, and preparing justifications before problems surface (P5). The authors' contribution is to recast accountability as a virtue to be cultivated through incentives rather than merely a mechanism enforced through punishment.","pith_inferences":["A natural extension the paper does not develop: if monetary bug bounties are perceived as controlling, the model predicts they would backfire, so the empirical payload depends on how developers interpret the reward's informational versus controlling character.","The same logic might apply to other AI governance instruments, such as ethics training or certification incentives, suggesting a general distinction between autonomy-supportive and coercive accountability mechanisms.","One testable extension is to compare bug bounties with non-monetary recognition awards, since Cognitive Evaluation Theory predicts the controlling aspect of tangible rewards can undermine the competence boost.","If the propositions hold, the paper's reframing could shift AI accountability debates from liability and punishment toward incentive design, with implications for regulation and platform governance."],"forward_implications":["Organizations could design AI accountability systems around bug bounties and similar reward programs instead of relying mainly on penalties.","If sanctions lower intrinsic motivation, heavy-handed accountability regimes may discourage exactly the proactive transparency they are meant to produce.","Proactive AI accountability behavior becomes an observable outcome—self-initiated documentation, bias reporting, early rectification—not just compliance with oversight.","The model gives empirical researchers a testable path from governance instruments to developer psychology to behavior."],"supporting_citations":[{"why":"Provides Self-Determination Theory and the claim that satisfying competence and autonomy increases intrinsic motivation.","marker":"Deci and Ryan 1985"},{"why":"Supplies the definitions of intrinsic and extrinsic motivation and the informational versus controlling distinction in Cognitive Evaluation Theory.","marker":"Ryan and Deci 2000"},{"why":"Meta-analytic evidence that extrinsic rewards affect intrinsic motivation, cited as strong empirical support for the need-to-motivation link.","marker":"Deci et al. 1999"},{"why":"Forty-year meta-analysis showing intrinsic motivation predicts persistence and performance, underpinning Proposition 5.","marker":"Cerasoli et al. 2014"},{"why":"Early experiment showing positive feedback enhances competence and intrinsic motivation, used to support the reward-to-competence proposition.","marker":"Deci 1971"},{"why":"Evidence that negative feedback and threats reduce intrinsic motivation, used to support the sanction propositions.","marker":"Deci and Cascio 1972"},{"why":"Establishes that proactivity in organizations derives from intrinsic motivation, supporting the final proposition.","marker":"Crant 2000"},{"why":"Identifies bug bounties as a mechanism for trustworthy AI development, grounding the paper's concrete reward example.","marker":"Brundage et al. 2020"},{"why":"Contextualizes AI accountability for socio-technical systems, which the paper adopts as its working definition.","marker":"Wieringa 2020"},{"why":"Defines accountability as an actor-forum relationship with consequences, providing the basis for including rewards alongside sanctions.","marker":"Bovens 2007"}],"fun_headline_variants":["Bug bounties may trigger proactive AI accountability","Rewards, not sanctions, key to AI developer action","How rewards could make AI developers more accountable","Positive incentives may spur proactive AI reporting","Study: Rewards beat penalties for AI accountability"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The model breaks if the concrete reward, a bug bounty, is experienced by developers as controlling pressure rather than as affirming their competence and autonomy, because the paper's own theory says controlling rewards reduce intrinsic motivation.","fun_headline_variants_meta":{"raw":{"variants":["Bug bounties may trigger proactive AI accountability","Rewards, not sanctions, key to AI developer action","How rewards could make AI developers more accountable","Positive incentives may spur proactive AI reporting","Study: Rewards beat penalties for AI accountability"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000203,"raw_usage":{"total_tokens":1327,"prompt_tokens":829,"completion_tokens":498,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":445,"completion_tokens_details":{"reasoning_tokens":428}},"tokens_in":445,"tokens_out":498,"duration_ms":4919,"temperature":1.0,"reasoning_tokens":428,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T11:14:18.073710+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A randomized scenario experiment with AI developers that compares a bug-bounty reward condition, a sanction condition, and a no-intervention control, measuring intrinsic motivation and self-initiated accountability actions; if rewarded developers do not show higher intrinsic motivation and more proactive behavior than the control, the paper's propositions fail.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Meta-analytic evidence that extrinsic rewards affect intrinsic motivation, cited as strong empirical support for the need-to-motivation link."}],"review_version":1}