Role of fairness, accountability, and transparency in algorithmic affordance

© 2019 Elsevier Ltd As algorithm-based services increase, social topics such as fairness, transparency, and accountability (FAT) must be addressed. This study conceptualizes such issues and examines how they influence the use and adoption of algorithm services. In particular, we investigate how trust is related to such issues and how trust influences the user experience of algorithm services. A multi-mixed method was used by integrating interpretive methods and surveys. The overall results show the heuristic role of fairness, accountability, and transparency, regarding their fundamental links to trust. Despite the importance of algorithms, no single testable definition has been observed. We reconstructed the understandings of algorithm and its affordance with user perception, invariant properties, and contextuality. The study concludes by arguing that algorithmic affordance offers a distinctive perspective on the conceptualization of algorithmic process. Individuals’ perceptions of FAT and how they actually perceive them are important topics for further study

Contextualizing privacy on health-related use of information technology

© 2019 Elsevier Ltd Privacy amid rapid digitalization of medical records is a critical ingredient to the success of electronic-based health service. This paper explores the potential roles of privacy attitudes concerning medical data, based on a large set of a national sample data (n = 2638) from the U.S. Health Information National Trend Survey. We examine the ways in which privacy concern and confidence are (a) mediated through one\u27s interest in sharing information with health professionals and (b) moderated by one\u27s medical condition and the reliance on Internet. Evidence from this study provides insights into the factors shaping health-related engagement in information technologies, helping us argue that privacy is a key predictor. Discussion offers interpretations of how people\u27s perceived need of medical data will mediate privacy concern, contextualizing the affordances of health technologies in future algorithmic applications

Targeting the insulin growth factor-1 receptor with fluorescent antibodies enables high resolution imaging of human pancreatic cancer in orthotopic mouse models.

The goal of the present study was to determine whether insulin-like growth factor-1 receptor (IGF-1R) antibodies, conjugated with bright fluorophores, could enable visualization of pancreatic cancer in orthotopic nude mouse models. IGF-1R antibody (clone 24-31) was conjugated with 550 nm or 650 nm fluorophores. Western blotting confirmed the expression of IGF-1R in Panc-1, BxPC3, and MIAPaCa-2 human pancreatic cancer cell lines. Labeling with fluorophore-conjugated IGF-1R antibody demonstrated fluorescent foci on the membrane of the pancreatic cancer cells. Subcutaneous Panc-1, BxPC-3, and MIA PaCa-2 tumors became fluorescent after intravenous administration of fluorescent IGF-1R antibodies. Orthotopically-transplanted BxPC-3 tumors became fluorescent with the conjugated IGF-1R antibodies, and were easily visible with intravital imaging. Gross and microscopic ex vivo imaging of resected pancreatic tumor and normal pancreas confirmed that fluorescence indeed came from the membrane of cancer cells, and it was stronger from the tumor than the normal tissue. The present study demonstrates that fluorophore-conjugated IGF-1R antibodies can visualize pancreatic cancer and it can be used with various imaging devices such as endoscopy and laparoscopy for diagnosis and fluorescence-guided surgery

Fluorescent-Antibody Targeting of Insulin-Like Growth Factor-1 Receptor Visualizes Metastatic Human Colon Cancer in Orthotopic Mouse Models.

Fluorescent-antibody targeting of metastatic cancer has been demonstrated by our laboratory to enable tumor visualization and effective fluorescence-guided surgery. The goal of the present study was to determine whether insulin-like growth factor-1 receptor (IGF-1R) antibodies, conjugated with bright fluorophores, could enable visualization of metastatic colon cancer in orthotopic nude mouse models. IGF-1R antibody (clone 24-31) was conjugated with 550 nm, 650 nm or PEGylated 650 nm fluorophores. Subcutaneous, orthotopic, and liver metastasis models of colon cancer in nude mice were targeted with the fluorescent IGF-1R antibodies. Western blotting confirmed the expression of IGF-1R in HT-29 and HCT 116 human colon cancer cell lines, both expressing green fluorescent protein (GFP). Labeling with fluorophore-conjugated IGF-1R antibody demonstrated fluorescent foci on the membrane of colon cancer cells. Subcutaneously- and orthotopically-transplanted HT-29-GFP and HCT 116-GFP tumors brightly fluoresced at the longer wavelengths after intravenous administration of fluorescent IGF-1R antibodies. Orthotopically-transplanted HCT 116-GFP tumors were brightly labeled by fluorescent IGF-1R antibodies such that they could be imaged non-invasively at the longer wavelengths. In an experimental liver metastasis model, IGF-1R antibodies conjugated with PEGylated 650 nm fluorophores selectively highlighted the liver metastases, which could then be non-invasively imaged. The IGF-1R fluorescent-antibody labeled liver metastases were very bright compared to the normal liver and the fluorescent-antibody label co-located with green fluorescent protein (GFP) expression of the colon cancer cells. The present study thus demonstrates that fluorophore-conjugated IGF-1R antibodies selectively visualize metastatic colon cancer and have clinical potential for improved diagnosis and fluorescence-guided surgery

The More Friends, the Less Political Talk? Predictors of Facebook Discussions Among College Students

Although previous research has indicated that Facebook users, especially young adults, can cultivate their civic values by talking about public matters with their Facebook friends, little research has examined the predictors of political discussion on Facebook. Using survey data from 442 college students in the United States, this study finds that individual characteristics and network size influence college students' expressive behavior on Facebook related to two controversial topics: gay rights issues and politics. In line with previous studies about offline political discussion, the results show that conflict avoidance and ambivalence about target issues are negatively associated with Facebook discussions. Perhaps the most interesting finding is that users who have a large number of Facebook friends are less likely to talk about politics and gay rights issues on Facebook despite having access to increasing human and information resources. Theoretical implications of these findings and future directions are addressed.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140346/1/cyber.2013.0477.pd

Intuitive Multilingual Audio-Visual Speech Recognition with a Single-Trained Model

We present a novel approach to multilingual audio-visual speech recognition tasks by introducing a single model on a multilingual dataset. Motivated by a human cognitive system where humans can intuitively distinguish different languages without any conscious effort or guidance, we propose a model that can capture which language is given as an input speech by distinguishing the inherent similarities and differences between languages. To do so, we design a prompt fine-tuning technique into the largely pre-trained audio-visual representation model so that the network can recognize the language class as well as the speech with the corresponding language. Our work contributes to developing robust and efficient multilingual audio-visual speech recognition systems, reducing the need for language-specific models.Comment: EMNLP 2023 Finding