In the age of artificial intelligence, Machine Learning (ML) has become a pervasive force, impacting countless aspects of our lives. As ML’s influence expands, concerns about its reliability and trustworthiness have intensified, with security and robustness emerging as significant challenges. For instance, it has been demonstrated that slight perturbations to a stop sign can cause ML classifiers to misidentify it as a speed limit sign, raising concerns about whether ML algorithms are suitable for real-world deployments. To tackle these issues, Responsible Machine Learning (Responsible ML) has emerged with a clear mission: to develop secure and robust ML algorithms. This dissertation aims to develop Responsible Machine Learning algorithms under real-world constraints. Specifically, recognizing the role of adversarial attacks in exposing security vulnerabilities and robustifying the ML methods, it lays down the foundation of Responsible ML by outlining a novel taxonomy of adversarial attacks within real-world settings, categorizing them into black-box target-specific, and target-agnostic attacks. Subsequently, it proposes potent adversarial attacks in each category, aiming to obtain effectiveness and efficiency. Transcending conventional boundaries, it then introduces the notion of causality into Responsible ML (a.k.a., Causal Responsible ML), presenting the causal adversarial attack. This represents the first principled framework to explain the transferability of adversarial attacks to unknown models by identifying their common source of vulnerabilities, thereby exposing the pinnacle of threat and vulnerability: conducting successful attacks on any model with no prior knowledge. Finally, acknowledging the surge of Generative AI, this dissertation explores Responsible ML for Generative AI. It introduces a novel adversarial attack that unveils their adversarial vulnerabilities and devises a strong defense mechanism to bolster the models’ robustness against potential attacks.

It is not merely an aggregation of static entities that a video clip carries, but alsoa variety of interactions and relations among these entities. Challenges still remain for a video captioning system to generate natural language descriptions focusing on the prominent interest and aligning with the latent aspects beyond observations. This work presents a Commonsense knowledge Anchored Video cAptioNing (dubbed as CAVAN) approach. CAVAN exploits inferential commonsense knowledge to assist the training of video captioning model with a novel paradigm for sentence-level semantic alignment. Specifically, commonsense knowledge is queried to complement per training caption by querying a generic knowledge atlas ATOMIC, and form the commonsense- caption entailment corpus. A BERT based language entailment model trained from this corpus then serves as a commonsense discriminator for the training of video captioning model, and penalizes the model from generating semantically misaligned captions. With extensive empirical evaluations on MSR-VTT, V2C and VATEX datasets, CAVAN consistently improves the quality of generations and shows higher keyword hit rate. Experimental results with ablations validate the effectiveness of CAVAN and reveals that the use of commonsense knowledge contributes to the video caption generation.