This thesis explores the generation of local explanations for already deployed machine learning models, aiming to identify optimal conditions for producing meaningful explanations considering both data and user requirements. The primary goal is to develop methods for generating explanations for any model while ensuring that these explanations remain faithful to the underlying model and comprehensible to the users. The thesis is divided into two parts. The first enhances a widely used rule-based explanation method. It then introduces a novel approach for evaluating the suitability of linear explanations to approximate a model. Additionally, it conducts a comparative experiment between two families of counterfactual explanation methods to analyze the advantages of one over the other. The second part focuses on user experiments to assess the impact of three explanation methods and two distinct representations. These experiments measure how users perceive their interaction with the model in terms of understanding and trust, depending on the explanations and representations. This research contributes to a better explanation generation, with potential implications for enhancing the transparency, trustworthiness, and usability of deployed AI systems.

Ronald Reagan's daughter, Patti Davis, weighed in on the age issue at the forefront of the 2024 election on Sunday and said presidential candidates probably should face cognitive tests while running for office. Before President Biden was elected, Reagan was the oldest person to be elected president, at the age of 69. "Now, obviously, the president is in his 80s, former President Trump, the frontrunner, is in his late 70s. Do you think there should be cognitive tests for people running for the highest office in the land?" And just what we know about what age can do, it doesn't always do that, but it would probably be a good idea.

With polls showing voters' concerns over Biden's age, there are growing calls for him to prove his mental fitness ahead of a rematch with Trump.Michael Reynolds/EFE/ZUMA The daughter of the once-oldest president, Ronald Reagan, who was 77 when he took office, thinks cognitive tests for presidential candidates would be "a good idea," she said in an interview that aired Sunday. "Just what we know about what age can do, it doesn't always do that, but it would probably be a good idea," Patti Davis said on NBC's Meet the Press, in response to a question from host Kristen Welker about whether she agreed with the prospect. WATCH: When Ronald Reagan was elected at 69, he was the oldest person ever to be elected president. Now his daughter, Patti Davis, says cognitive tests would be a "good idea." Davis: "My father was 77 when he left office after two terms. It seems so young now, doesn't it?"

In a report sure to find its place in the annals of politically damaging exonerations, Robert Hur, the special counsel appointed to investigate Joe Biden's handling of classified documents, cleared the President of wrongdoing, and explicitly distinguished his behavior from Donald Trump's more egregious misconduct in a similar case. But Hur, a Republican, also noted that he didn't recommend charges in part because Biden would likely come across to a jury as a "well-meaning, elderly man with a poor memory." Among his claims was that Biden couldn't recall when he'd been Vice-President, or when his son Beau had died, "even within several years." Biden's supporters saw the language as a gratuitous partisan attack, a speculative salvo far outside the prosecutor's purview; his lawyers said it was "highly prejudicial." Clearly sensing the precarity of the moment, the White House called a press conference at which Biden forcefully disputed Hur's characterization.

In July, 2013, Will Shortz, the New York Times' longtime puzzle editor, asked me to be his assistant. I had just graduated from college, and, to my mind, the invitation had little rationale. It arrived on the heels of minimal correspondence: two e-mails in which Shortz had accepted two of my puzzles, with minor revisions. I doubted his motives for hiring me as much as my qualifications for the job. Surely, there were many more prolific and talented crossword constructors who could have assisted him. The only thing that distinguished me, I thought, was my gender: I was a young woman, and this was a field rife with men.

Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are "small" but "realistic" semantic changes of the image changing the classifier decision. Current approaches for the generation of VCEs are restricted to adversarially robust models and often contain non-realistic artefacts, or are limited to image classification problems with few classes. In this paper, we overcome this by generating Diffusion Visual Counterfactual Explanations (DVCEs) for arbitrary ImageNet classifiers via a diffusion process. Two modifications to the diffusion process are key for our DVCEs: first, an adaptive parameterization, whose hyperparameters generalize across images and models, together with distance regularization and late start of the diffusion process, allow us to generate images with minimal semantic changes to the original ones but different classification. Second, our cone regularization via an adversarially robust model ensures that the diffusion process does not converge to trivial non-semantic changes, but instead produces realistic images of the target class which achieve high confidence by the classifier.

For the synthetic-to-real domain generalization (DG), we use one of the synthetic datasets (GTAV [12] or SYNTHIA [13]) as the source domain and evaluate the model performance on three real-world datasets (CityScapes [2], BDD-100K [16], and Mapillary [11]). GTAV [12] contains 24,966 images with the size of 1914 1052. It is splited into 12,403, 6,382, and 6,181 images for training, validating, and testing. SYNTHIA [13] contains 9,400 images of 960 720, where 6,580 images are used for training. We use the validation sets of the three real-world datasets for evaluation.

Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, current approaches are limited by either a significant run-time or the need for the user to specify a low-cost approximation to the full posterior. We propose a Bayesian coreset construction algorithm that first selects a uniformly random subset of data, and then optimizes the weights using a novel quasi-Newton method. Our algorithm is a simple to implement, black-box method, that does not require the user to specify a low-cost posterior approximation. It is the first to come with a general high-probability bound on the KL divergence of the output coreset posterior. Experiments demonstrate that our method provides significant improvements in coreset quality against alternatives with comparable construction times, with far less storage cost and user input required.

The United States has condemned growing violence in the Democratic Republic of the Congo (DRC), blaming an armed group it says is backed by neighbouring Rwanda. Fighting has flared in recent days in the eastern part of the DRC between the M23 rebel group and government forces, resulting in dozens of soldiers and civilians being killed or wounded. The fighting has also pushed tens of thousands of civilians to flee towards the eastern city of Goma, which is located between Lake Kivu and the border with Rwanda. "This escalation has increased the risk to millions of people already exposed to human rights abuses including displacement, deprivation, and attacks," US State Department spokesman Matthew Miller said in a statement. "The United States condemns Rwanda's support for the M23 armed group and calls on Rwanda to immediately withdraw all Rwanda Defense Force personnel from the DRC and remove its surface-to-air missile systems, which threaten the lives of civilians, UN and other regional peacekeepers, humanitarian actors, and commercial flights in eastern DRC," Miller added.

Many elections are conducted sequentially, where interim results are known to the electorate and can be used by voters to inform their decisions. Given empirical work showing that voters tend to vote for the leading candidates when votes are public [MM+15; ZM+15; RR+17; MG+20; AG22], it is natural to consider the strategic aspect of choosing when to vote in such settings. The power of strategic vote timing is illustrated by the commonplace show-of-hands vote: "early bird" voters may sway undecided voters to follow in their direction. Furthermore, if there are costs associated with voting (e. g., having to commute to a distant polling station), waiting to observe interim results allows voters to save costs, if their preferred outcome appears to have garnered enough support to win. In particular, previous work found that voting costs affect voter turnout in blockchain governance voting, where votes are irrevocable and interim results are public [DY+23; MP+23]. This also applies to settings in which costs may be implicit, such as in democratic deliberation dialogues [FP+23] and social networks [AB+12], where voters may face social consequences if their vote does not conform to the accepted norms (e. g., liking a controversial social media post). 1