Overcoming Algorithm Aversion: People will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them

Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion. In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Stuides 1-3). In fact, our results suggest that participants\u27 preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants\u27 preference for modifiable was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control—even a slight amount—over an imperfect algorithm\u27s forecast

Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err

Research shows that evidence-based algorithms more accurately predict the future than do human forecasters. Yet when forecasters are deciding whether to use a human forecaster or a statistical algorithm, they often choose the human forecaster. This phenomenon, which we call algorithm aversion, is costly, and it is important to understand its causes. We show that people are especially averse to algorithmic forecasters after seeing them perform, even when they see them outperform a human forecaster. This is because people more quickly lose confidence in algorithmic than human forecasters after seeing them make the same mistake. In 5 studies, participants either saw an algorithm make forecasts, a human make forecasts, both, or neither. They then decided whether to tie their incentives to the future predictions of the algorithm or the human. Participants who saw the algorithm perform were less confident in it, and less likely to choose it over an inferior human forecaster. This was true even among those who saw the algorithm outperform the human

Adolescent perceptions of parental privacy invasion and adolescent secrecy:An illustration of Simpson's paradox

Adolescents' secrecy is intertwined with perception of parents' behaviors as acts of privacy invasion. It is currently untested, however, how this transactional process operates at the within-person level-where these causal processes take place. Dutch adolescents (n = 244, Mage = 13.84, 38.50% boys) reported three times on perceived parental privacy invasion and secrecy. Cross-lagged panel models (CLPM) confirmed earlier findings. Privacy invasion predicted increased secrecy, but a reverse effect was found from increased secrecy to increased privacy invasion. Controlling for confounding positive group-level associations with a novel random intercept CLPM, negative within-person associations were found. Higher levels of secrecy predicted lower levels of privacy invasive behaviors at the within-person level. These opposing findings within- versus between-persons illustrate a Simpson's paradox

Misplaced Trust: Measuring the Interference of Machine Learning in Human Decision-Making

ML decision-aid systems are increasingly common on the web, but their successful integration relies on people trusting them appropriately: they should use the system to fill in gaps in their ability, but recognize signals that the system might be incorrect. We measured how people's trust in ML recommendations differs by expertise and with more system information through a task-based study of 175 adults. We used two tasks that are difficult for humans: comparing large crowd sizes and identifying similar-looking animals. Our results provide three key insights: (1) People trust incorrect ML recommendations for tasks that they perform correctly the majority of the time, even if they have high prior knowledge about ML or are given information indicating the system is not confident in its prediction; (2) Four different types of system information all increased people's trust in recommendations; and (3) Math and logic skills may be as important as ML for decision-makers working with ML recommendations.Comment: 10 page

fMRI Activities in the Emotional Cerebellum: A Preference for Negative Stimuli and Goal-Directed Behavior

Several studies indicate that the cerebellum might play a role in experiencing and/or controlling emphatic emotions, but it remains to be determined whether there is a distinction between positive and negative emotions, and, if so, which specific parts of the cerebellum are involved in these types of emotions. Here, we visualized activations of the cerebellum and extracerebellar regions using high-field fMRI, while we asked participants to observe and imitate images with pictures of human faces expressing different emotional states or with moving geometric shapes as control. The state of the emotions could be positive (happiness and surprise), negative (anger and disgust), or neutral. The positive emotional faces only evoked mild activations of crus 2 in the cerebellum, whereas the negative emotional faces evoked prominent activations in lobules VI and VIIa in its hemispheres and lobules VIII and IX in the vermis. The cerebellar activations associated with negative emotions occurred concomitantly with activations of mirror neuron domains such as the insula and amygdala. These data suggest that the potential role of the cerebellum in control of emotions may be particularly relevant for goal-directed behavior that is required for observing and reacting to another person’s (negative) expressions

The effect of (neo)adjuvant chemotherapy on long-term survival outcomes in patients with invasive lobular breast cancer treated with endocrine therapy:A retrospective cohort study

Background: Despite histological and molecular differences between invasive lobular carcinoma (ILC) and invasive carcinoma of no special type, according to national treatment guidelines no distinction is made regarding the use of (neo)adjuvant chemotherapy. Studies on the long-term outcome of chemotherapy in patients with ILC are scarce and show inconclusive results. Methods:All patients with estrogen receptor (ER)–positive, human epidermal growth factor receptor 2 (HER2)–negative ILC with an indication for chemotherapy treated with adjuvant endocrine therapy were selected from the Erasmus Medical Center Breast Cancer database. Cox proportional hazards models were used to estimate the effect of chemotherapy on recurrence-free survival (RFS), breast cancer–specific survival (BCSS), and overall survival (OS). Results: A total of 520 patients were selected, of whom 379 were treated with chemotherapy and 141 were not. Patients in the chemotherapy group were younger (51 vs. 61 years old; p &lt;.001), had a higher T status (T3+, 33% vs. 14%; p &lt;.001), and more often had lymph node involvement (80% vs. 49%; p &lt;.001) in comparison to the no-chemotherapy group. After adjusting for confounders, chemotherapy treatment was not associated with better RFS (hazard ratio [HR], 1.20; 95% confidence interval [CI], 0.63–2.31), BCSS (HR, 1.24; 95% CI, 0.60–2.58), or OS (HR, 0.97; 95% CI, 0.56–1.66). This was also reflected by adjusted Cox survival curves in the chemotherapy versus no-chemotherapy group for RFS (75% vs. 79%), BCSS (80% vs. 84%), and OS (72% vs. 71%). Conclusions:Chemotherapy is not associated with improved RFS, BCSS, or OS for patients with ER+/HER2− ILC treated with adjuvant endocrine therapy and with an indication for chemotherapy.</p

The effect of hexose ratios on metabolite production in Saccharomyces cerevisiae strains obtained from the spontaneous fermentation of mezcal

Mezcal from Tamaulipas (Me´xico) is produced by spontaneous alcoholic fermentation using Agave spp. musts, which are rich in fructose. In this study eight Saccharomyces cerevisiae isolates obtained at the final stage of fermentation from a traditional mezcal winery were analysed in three semisynthetic media. Medium M1 had a sugar content of 100 g l-1 and a glucose/fructose (G/F) of 9:1. Medium M2 had a sugar content of 100 g l-1 and a G/F of 1:9. Medium M3 had a sugar content of 200 g l-1 and a G/F of 1:1. In the three types of media tested, the highest ethanol yield was obtained from the glucophilic strain LCBG-3Y5, while strain LCBG-3Y8 was highly resistant to ethanol and the most fructophilic of the mezcal strains. Strain LCBG-3Y5 produced more glycerol (4.4 g l-1) and acetic acid (1 g l-1) in M2 than in M1 (1.7 and 0.5 g l-1, respectively), and the ethanol yields were higher for all strains in M1 except for LCBG-3Y5, -3Y8 and the Fermichamp strain. In medium M3, only the Fermichamp strain was able to fully consume the 100 g of fructose l-1 but left a residual 32 g of glucose l-1. Regarding the hexose transporters, a high number of amino acid polymorphisms were found in the Hxt1p sequences. Strain LCBG-3Y8 exhibited eight unique amino acid changes, followed by the Fermichamp strain with three changes. In Hxt3p, we observed nine amino acid polymorphisms unique for the Fermichamp strain and five unique changes for the mezcal strains

Following wrong suggestions: self-blame in human and computer scenarios

This paper investigates the specific experience of following a suggestion by an intelligent machine that has a wrong outcome and the emotions people feel. By adopting a typical task employed in studies on decision-making, we presented participants with two scenarios in which they follow a suggestion and have a wrong outcome by either an expert human being or an intelligent machine. We found a significant decrease in the perceived responsibility on the wrong choice when the machine offers the suggestion. At present, few studies have investigated the negative emotions that could arise from a bad outcome after following the suggestion given by an intelligent system, and how to cope with the potential distrust that could affect the long-term use of the system and the cooperation. This preliminary research has implications in the study of cooperation and decision making with intelligent machines. Further research may address how to offer the suggestion in order to better cope with user's self-blame.Comment: To be published in the Proceedings of IFIP Conference on Human-Computer Interaction (INTERACT)201

Should we welcome robot teachers?

Abstract Current uses of robots in classrooms are reviewed and used to characterise four scenarios: (s1) Robot as Classroom Teacher; (s2) Robot as Companion and Peer; (s3) Robot as Care-eliciting Companion; and (s4) Telepresence Robot Teacher. The main ethical concerns associated with robot teachers are identified as: privacy; attachment, deception, and loss of human contact; and control and accountability. These are discussed in terms of the four identified scenarios. It is argued that classroom robots are likely to impact children’s’ privacy, especially when they masquerade as their friends and companions, when sensors are used to measure children’s responses, and when records are kept. Social robots designed to appear as if they understand and care for humans necessarily involve some deception (itself a complex notion), and could increase the risk of reduced human contact. Children could form attachments to robot companions (s2 and s3), or robot teachers (s1) and this could have a deleterious effect on their social development. There are also concerns about the ability, and use of robots to control or make decisions about children’s behaviour in the classroom. It is concluded that there are good reasons not to welcome fully fledged robot teachers (s1), and that robot companions (s2 and 3) should be given a cautious welcome at best. The limited circumstances in which robots could be used in the classroom to improve the human condition by offering otherwise unavailable educational experiences are discussed

Grumpy or depressed? Disentangling typically developing adolescent mood from prodromal depression using experience sampling methods

Introduction: This study aimed at differentiating normative developmental turmoil from prodromal depressive symptoms in adolescence. Method: Negative and positive mood (daily) in different contexts (friends, home, school), and (subsequent) depressive symptoms were assessed in Dutch adolescents. Results & conclusion: Mixture modeling on one cross-sectional study, using a newly developed questionnaire (CSEQ; subsample 1a; n = 571; girls 55.9%; Mage = 14.17) and two longitudinal datasets with Experience Sampling Methods data (subsample 1b: n = 241; Mage = 13.81; 62.2% girls, sample 2: n = 286; 59.7% girls; Mage = 14.19) revealed three mood profiles: 18–24% "happy", 43–53% "typically developing", and 27–38% "at-risk". Of the “at-risk” profile between 12.5% and 25% of the adolescents scored above the clinical cut-off for depression. These mood profiles predicted later depressive symptoms, while controlling for earlier symptoms. In subsample 1b, parents were not always aware of the mental health status of their adolescent