# Trustworthiness

(Redirected from Trust-worthiness)

The quality of being trust-worthy can be related to the following three properties:

# Security

In the context of data science, one should confine the definition of the word secure or security within the realm of Cryptographically-secure. If the data can only be accessed by someone who holds a piece of secret, then, data security is protected by the possession of such secret. Since people who don't have the secret simply have no access to the data content, therefore, the degree of security is determined by the strength of the secret protection.

# Irrefutability

Falsifiability(Q220888), is the opposite statement of non-falsifiable, or irrefutable. A theory or hypothesis is falsifiable (or refutable) if it can be logically contradicted by an empirical test that can potentially be executed with existing technologies.

# Excerpts from Wikipedia

One of the questions in scientific method is: how does one move from observations to scientific laws? This is the problem of induction. Suppose we want to put the hypothesis that all swans are white to the test. We come across a white swan. We cannot validly argue (or induce) from "here is a white swan" to "all swans are white"; doing so would require a logical fallacy such as, for example, affirming the consequent.

Popper's idea to solve this problem is that while it is impossible to verify that every swan is white, finding a single black swan shows that not every swan is white. We might tentatively accept the proposal that every swan is white, while looking out for examples of non-white swans that would show our conjecture to be false. Falsification uses the valid inference modus tollens: if from a law $L$ we logically deduce $Q$ , but what is observed is $\neg Q$ , we infer that the law $L$ is false. For example, given the statement $L=$ "all swans are white", we can deduce $Q=$ "the specific swan here is white", but if what is observed is $\neg Q=$ "the specific swan here is not white" (say black), then "all swans are white" is false. More accurately, the statement $Q$ that can be deduced is broken into an initial condition and a prediction as in $C\Rightarrow P$ in which $C=$ "the thing here is a swan" and $P=$ "the thing here is a white swan". If what is observed is C being true while P is false (formally, $C\wedge \neg P$ ), we can infer that the law is false.

# Related to Data Science

If the data content includes information that indicates data sources, while the data set can be verified using publicly-known Tamperproofing techniques.

# Availability

Some thing becomes available, when the resources is available over spacetime through Backup and Restore as a required reflexive procedure.