Position: Explainable AI is Causality in Disguise

Dimensions overall score 2.0

• The persistent discord in Explainable AI (XAI) arises from the absence of causal grounding, not from an absent ground truth.technical 90%
  Evidencepartial

  This position paper posits that the persistent discord in XAI arises not from an absent ground truth but from a ground truth that exists, albeit as an elusive and challenging target: the causal model that governs the relevant system.

  Implicationpartial

  Directly stated as the core position of the paper in the abstract and introduction.

  Verificationpartial

  partial

• Causal models are both necessary and sufficient for achieving Explainable AI.technical 85%
  Evidencepartial

  By reframing XAI queries about data, models, or decisions as causal inquiries, we prove the necessity and sufficiency of causal models for XAI.

  Implicationpartial

  Explicitly stated in the abstract and formalized in a theorem (Theorem 4.4).

  Verificationpartial

  partial

• Core XAI questions (e.g., 'Why does the model make a specific decision?') map directly onto Pearl's Ladder of Causation, revealing XAI as fundamentally causal.method 80%
  Evidencepartial

  By mapping these directly onto Pearl’s Ladder of Causation, we reveal that solving XAI fundamentally requires answering causal inquiries.

  Implicationpartial

  Strongly supported by Figure 1 and its description, which categorizes XAI questions and maps them to causal levels.

  Verificationpartial

  partial

• Without causal grounding, XAI remains unmoored and subject to conflicting metrics and unresolved debates.limitation 75%
  Evidencepartial

  We contend that without this causal grounding, XAI remains unmoored.

  Implicationpartial

  Directly stated as a contention following from the paper's thesis, supported by the abstract's description of current challenges.

  Verificationpartial

  partial

• Current concept-based explanation methods like TCAV are limited because they rely on known, predefined concepts and cannot discover new, relevant ones.limitation 85%
  Evidencepartial

  However, TCAV is limited to known concepts and cannot discover new, relevant concepts—the “unknown unknowns”—that may be crucial for understanding the model’s behavior.

  Implicationpartial

  Directly stated critique of a specific method with a clear limitation identified.

  Verificationpartial

  partial

• The field of XAI is currently fragmented, with a lack of consensus on how to achieve explainability, leading to reliance on surveys of surveys.result 90%
  Evidencepartial

  The demand for Explainable AI (XAI) has triggered an explosion of methods, producing a landscape so fragmented that we now rely on surveys of surveys. Yet, fundamental challenges persist... The only consensus on how to achieve explainability is a lack of one.

  Implicationpartial

  Explicitly stated in the abstract as the current state of the field.

  Verificationpartial

  partial

• Advancing XAI requires the community to converge around advanced concept and causal discovery methods.method 80%
  Evidencepartial

  Ultimately, we encourage the community to converge around advanced concept and causal discovery to escape this entrenched uncertainty.

  Implicationpartial

  Directly stated as the paper's concluding encouragement and proposal.

  Verificationpartial

  partial

• Causal representation learning is a promising approach for capturing both the concept space and causal structure needed for XAI.method 70%
  Evidencepartial

  We propose leveraging advances in causal representation learning (Bengio et al., 2019; Schölkopf et al., 2021), which strive to capture both the concept space and causal stru

  Implicationpartial

  Proposed as a method to enhance concept discovery, but presented as a suggestion rather than a proven result.

  Verificationpartial

  partial

Competitive landscape

This paper argues that explainable AI can only be achieved by grounding it in causal models, suggesting a shift in research focus towards causal discovery.